diff --git "a/out-jvm/train.jsonl" "b/out-jvm/train.jsonl" new file mode 100644--- /dev/null +++ "b/out-jvm/train.jsonl" @@ -0,0 +1,10000 @@ +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO cultural_events SELECT college, deaths, has_disability FROM coal WHERE college > 484\");\n", "labels": {"reads": [{"table": "coal", "columns": ["college", "deaths", "has_disability"]}], "writes": [{"table": "cultural_events", "columns": ["college", "deaths", "has_disability"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO ads_payments_hourly SELECT a.start_therapy, b.date_and_date FROM hospital_equipment a JOIN sponsorship_donations b ON a.distributorid = b.distributorid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "hospital_equipment", "columns": null}, {"table": "sponsorship_donations", "columns": null}], "writes": [{"table": "ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table projects --columns pcp,emp_fname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "projects", "columns": ["pcp", "emp_fname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO us_cities SELECT stageposition, group_id, charging_level FROM list WHERE stageposition > 396\");\n", "labels": {"reads": [{"table": "list", "columns": ["stageposition", "group_id", "charging_level"]}], "writes": [{"table": "us_cities", "columns": ["stageposition", "group_id", "charging_level"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO transportation_trips SELECT stu_phone, resource, language, animal_type FROM directors WHERE stu_phone > 345\")\n", "labels": {"reads": [{"table": "directors", "columns": ["stu_phone", "resource", "language", "animal_type"]}], "writes": [{"table": "transportation_trips", "columns": ["stu_phone", "resource", "language", "animal_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"episodes\")\nsrc.write.insertInto(\"mart_cart_item_di\", overwrite=True)\n", "labels": {"reads": [{"table": "episodes", "columns": null}], "writes": [{"table": "mart_cart_item_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table exhibitionsartworks --target-dir /tmp/land\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO affiliated_with SELECT restaurant, closure_authorised_by_staff_id, plant_name FROM ads.ads_exposure_daily WHERE restaurant > 493\"\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": ["restaurant", "closure_authorised_by_staff_id", "plant_name"]}], "writes": [{"table": "affiliated_with", "columns": ["restaurant", "closure_authorised_by_staff_id", "plant_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"chemicals_annual\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"field4_precip\")\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": null}], "writes": [{"table": "field4_precip", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 31;\nEOF\n", "labels": {"reads": [{"table": "marine_life_research", "columns": ["attendee_age", "player_api_id", "parent_organization_id"]}], "writes": [{"table": "donation", "columns": ["attendee_age", "player_api_id", "parent_organization_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO adrprograms SELECT a.reaction_time, b.unavailable FROM scores a JOIN building b ON a.conferenceid = b.conferenceid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "scores", "columns": null}, {"table": "building", "columns": null}], "writes": [{"table": "adrprograms", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO field SELECT sustainable_practice, book_title FROM sustainable_warehouses WHERE sustainable_practice > 140\");\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": ["sustainable_practice", "book_title"]}], "writes": [{"table": "field", "columns": ["sustainable_practice", "book_title"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"election\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"department_publications\")\n", "labels": {"reads": [{"table": "election", "columns": null}], "writes": [{"table": "department_publications", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.added_date > 233).all()\n# src table: soccer_teams\nengine.execute(\"INSERT INTO vr_tech SELECT * FROM soccer_teams\")\n", "labels": {"reads": [{"table": "soccer_teams", "columns": null}], "writes": [{"table": "vr_tech", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"investor\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "investor", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"reverselogisticstransactions\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"chemical_processes\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "chemical_processes", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO sports SELECT poll_source, book_club_id, species_id, bioprocess_id FROM parts WHERE poll_source > 167\")\n", "labels": {"reads": [{"table": "parts", "columns": ["poll_source", "book_club_id", "species_id", "bioprocess_id"]}], "writes": [{"table": "sports", "columns": ["poll_source", "book_club_id", "species_id", "bioprocess_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gameattendance\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"food_assistance\")\n", "labels": {"reads": [{"table": "gameattendance", "columns": null}], "writes": [{"table": "food_assistance", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"climate_data\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "climate_data", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO iron SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO mart.shipments_delta SELECT staff_name, open_date, activity_id, actor_id FROM imagery_archive WHERE staff_name > 454\");\n", "labels": {"reads": [{"table": "imagery_archive", "columns": ["staff_name", "open_date", "activity_id", "actor_id"]}], "writes": [{"table": "mart.shipments_delta", "columns": ["staff_name", "open_date", "activity_id", "actor_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_table(ctx, \"ref_document_status\")\nwrite_to_target(df, \"latam_schema.education_budget\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ref_document_status", "columns": null}], "writes": [{"table": "latam_schema.education_budget", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural.bus_trips\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"weather\")\n", "labels": {"reads": [{"table": "rural.bus_trips", "columns": null}], "writes": [{"table": "weather", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dorm\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dorm", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO defense_contractors SELECT * FROM legacy\nspark.sql(\"INSERT INTO policy_feedback SELECT negotiation_date, treatment_type FROM shared_ebikes WHERE negotiation_date > 456\")\n", "labels": {"reads": [{"table": "shared_ebikes", "columns": ["negotiation_date", "treatment_type"]}], "writes": [{"table": "policy_feedback", "columns": ["negotiation_date", "treatment_type"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dws.dws_orders\"\n", "labels": {"reads": [{"table": "dws.dws_orders", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ingredientsvegancrueltyfree SELECT * FROM legacy\nspark.sql(\"INSERT INTO user_workouts_march SELECT last_checkup_date, session_date FROM subway WHERE last_checkup_date > 370\")\n", "labels": {"reads": [{"table": "subway", "columns": ["last_checkup_date", "session_date"]}], "writes": [{"table": "user_workouts_march", "columns": ["last_checkup_date", "session_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO tourism_centers SELECT * FROM legacy\nspark.sql(\"INSERT INTO automation_tech SELECT startup_name, mediatypeid FROM soccer_teams WHERE startup_name > 105\")\n", "labels": {"reads": [{"table": "soccer_teams", "columns": ["startup_name", "mediatypeid"]}], "writes": [{"table": "automation_tech", "columns": ["startup_name", "mediatypeid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO fairness_scores (official_native_language, typical_buying_price) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "fairness_scores", "columns": ["official_native_language", "typical_buying_price"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO circular_economy_companies (movement, emp_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "circular_economy_companies", "columns": ["movement", "emp_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO products SELECT area_id, causename, device_id FROM route WHERE area_id > 315\")\n", "labels": {"reads": [{"table": "route", "columns": ["area_id", "causename", "device_id"]}], "writes": [{"table": "products", "columns": ["area_id", "causename", "device_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ai_ethics_policies SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table renewableenergy --columns survey_id,financially_capable --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "renewableenergy", "columns": ["survey_id", "financially_capable"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"hospitallocations\")\nsrc.write.insertInto(\"waterconservationbudget\", overwrite=True)\n", "labels": {"reads": [{"table": "hospitallocations", "columns": null}], "writes": [{"table": "waterconservationbudget", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO healthcare_system SELECT workshop_group_id, opname, firstdonationdate FROM visits WHERE workshop_group_id > 75\"\n", "labels": {"reads": [{"table": "visits", "columns": ["workshop_group_id", "opname", "firstdonationdate"]}], "writes": [{"table": "healthcare_system", "columns": ["workshop_group_id", "opname", "firstdonationdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT building_description, co2_amount FROM auctions LIMIT 1\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "auctions", "columns": ["building_description", "co2_amount"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table stories --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO machinery SELECT birth_date, character, preferred_foot FROM acceptance WHERE birth_date > 56\");\n", "labels": {"reads": [{"table": "acceptance", "columns": ["birth_date", "character", "preferred_foot"]}], "writes": [{"table": "machinery", "columns": ["birth_date", "character", "preferred_foot"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT quantity, workout_id FROM ods.ods_member_point_delta LIMIT 451\")\nrows = cur.fetchall()\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "ods.ods_member_point_delta", "columns": ["quantity", "workout_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gradeconversion\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"retail_workers_union\")\n", "labels": {"reads": [{"table": "gradeconversion", "columns": null}], "writes": [{"table": "retail_workers_union", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table milestones --target-dir /tmp/land\n", "labels": {"reads": [{"table": "milestones", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_table(ctx, \"mart.mart_shipments_hourly\")\nsink_to_sink(df, \"public_transportation_routes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": null}], "writes": [{"table": "public_transportation_routes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"user_workouts_march\")\nsrc.write.insertInto(\"pilot\", overwrite=True)\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": null}], "writes": [{"table": "pilot", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dailystreams SELECT date_of_latest_revision, restypename, enable_third_party_ads FROM visits_restaurant WHERE date_of_latest_revision > 125\"\n", "labels": {"reads": [{"table": "visits_restaurant", "columns": ["date_of_latest_revision", "restypename", "enable_third_party_ads"]}], "writes": [{"table": "dailystreams", "columns": ["date_of_latest_revision", "restypename", "enable_third_party_ads"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO call_volume SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ticketsales SELECT a.police_force, b.complaint_type_code FROM channel a JOIN ods.sessions b ON a.money_requested = b.money_requested\"\n", "labels": {"reads": [{"table": "channel", "columns": null}, {"table": "ods.sessions", "columns": null}], "writes": [{"table": "ticketsales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO club SELECT playergameid, change_date FROM energy_consumption WHERE playergameid > 136\"\n", "labels": {"reads": [{"table": "energy_consumption", "columns": ["playergameid", "change_date"]}], "writes": [{"table": "club", "columns": ["playergameid", "change_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT brand_id, manufacturer_id FROM surveylocations LIMIT 163\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "surveylocations", "columns": ["brand_id", "manufacturer_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO digital_assets SELECT a.avg_speed, b.date_became_customer FROM criminalcases a JOIN climate_communication b ON a.marketing_region_descriptrion = b.marketing_region_descriptrion\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "criminalcases", "columns": null}, {"table": "climate_communication", "columns": null}], "writes": [{"table": "digital_assets", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"trust\");\ndf.write().mode(\"overwrite\").saveAsTable(\"worker_union\");\n", "labels": {"reads": [{"table": "trust", "columns": null}], "writes": [{"table": "worker_union", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 76;\nSQL\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": ["emp_dob", "image_name"]}, {"table": "venture", "columns": ["is_dessert", "site_id", "dose", "article_id"]}], "writes": [{"table": "smart_city_projects", "columns": ["is_dessert", "site_id", "dose", "article_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"singer\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"public.forest_stats\")\n", "labels": {"reads": [{"table": "singer", "columns": null}], "writes": [{"table": "public.forest_stats", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO platform SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO hospitallocations SELECT faculty, bname FROM player_coach WHERE faculty > 197\"\n", "labels": {"reads": [{"table": "player_coach", "columns": ["faculty", "bname"]}], "writes": [{"table": "hospitallocations", "columns": ["faculty", "bname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO papers SELECT city_code, budget_allocation, student FROM aquatic_farms WHERE city_code > 51\"\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": ["city_code", "budget_allocation", "student"]}], "writes": [{"table": "papers", "columns": ["city_code", "budget_allocation", "student"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO cmi_cross_references SELECT * FROM legacy\nspark.sql(\"INSERT INTO safety_incidents_india SELECT trip_type, fleet_series FROM medicine WHERE trip_type > 461\")\n", "labels": {"reads": [{"table": "medicine", "columns": ["trip_type", "fleet_series"]}], "writes": [{"table": "safety_incidents_india", "columns": ["trip_type", "fleet_series"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model docking depends on infrastructure_projects\ndbt run --models docking --vars '{\"src\":\"infrastructure_projects\"}'\n", "labels": {"reads": [{"table": "infrastructure_projects", "columns": null}], "writes": [{"table": "docking", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO market_trends SELECT a.shoe_brand, b.co2_emissions FROM draft_copies a JOIN streams b ON a.country_id = b.country_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "draft_copies", "columns": null}, {"table": "streams", "columns": null}], "writes": [{"table": "market_trends", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 140;\nSQL\n", "labels": {"reads": [{"table": "route", "columns": ["class", "balance"]}, {"table": "volunteerprograms", "columns": ["firstdonationdate", "trade"]}], "writes": [{"table": "arctic_weather", "columns": ["firstdonationdate", "trade"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 102;\nEOF\n", "labels": {"reads": [{"table": "contractnegotiations", "columns": ["position", "shariah_compliant_investment_amount", "coupon_amount", "visit_month"]}], "writes": [{"table": "school_bus", "columns": ["position", "shariah_compliant_investment_amount", "coupon_amount", "visit_month"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamesessions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"performers\")\n", "labels": {"reads": [{"table": "gamesessions", "columns": null}], "writes": [{"table": "performers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO stg.stg_inventory_hourly SELECT form_id, menu_category, safety_record, asset_id FROM district WHERE form_id > 108\");\n", "labels": {"reads": [{"table": "district", "columns": ["form_id", "menu_category", "safety_record", "asset_id"]}], "writes": [{"table": "stg.stg_inventory_hourly", "columns": ["form_id", "menu_category", "safety_record", "asset_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO manager_award SELECT a.donation_date, b.vulnerability_score FROM brandrevenue a JOIN user_interests b ON a.functional_area_description = b.functional_area_description\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "brandrevenue", "columns": null}, {"table": "user_interests", "columns": null}], "writes": [{"table": "manager_award", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO ads.refunds SELECT home_city, shippingmethod, maxoccupancy FROM ods.clicks_full WHERE home_city > 125\")\n", "labels": {"reads": [{"table": "ods.clicks_full", "columns": ["home_city", "shippingmethod", "maxoccupancy"]}], "writes": [{"table": "ads.refunds", "columns": ["home_city", "shippingmethod", "maxoccupancy"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO customer SELECT wellname, providerid, price, strain_name FROM mart_campaigns_delta WHERE wellname > 207\");\n", "labels": {"reads": [{"table": "mart_campaigns_delta", "columns": ["wellname", "providerid", "price", "strain_name"]}], "writes": [{"table": "customer", "columns": ["wellname", "providerid", "price", "strain_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"battery_storage\");\ndf.write().mode(\"overwrite\").saveAsTable(\"atlantic_ocean_fish\");\n", "labels": {"reads": [{"table": "battery_storage", "columns": null}], "writes": [{"table": "atlantic_ocean_fish", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT platform, visitorid FROM cultural_competency LIMIT 290\")\nimport logging\nspark.sql(\"INSERT INTO military_personnel SELECT zip_code, round_number FROM traffic_accidents WHERE zip_code > 345\")\n", "labels": {"reads": [{"table": "cultural_competency", "columns": ["platform", "visitorid"]}, {"table": "traffic_accidents", "columns": ["zip_code", "round_number"]}], "writes": [{"table": "military_personnel", "columns": ["zip_code", "round_number"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"dancefunding\")\nexport_to_store(df, \"communityevents\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dancefunding", "columns": null}], "writes": [{"table": "communityevents", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO stg.stg_coupon_use_di SELECT dapp_name, grape, energy_efficiency_kwh_m2_year FROM artistsales WHERE dapp_name > 99\")\n", "labels": {"reads": [{"table": "artistsales", "columns": ["dapp_name", "grape", "energy_efficiency_kwh_m2_year"]}], "writes": [{"table": "stg.stg_coupon_use_di", "columns": ["dapp_name", "grape", "energy_efficiency_kwh_m2_year"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO organization_contact_individuals SELECT genre, feature_id, datetime_detention_start, is_unionized FROM genetic.projects WHERE genre > 93\"\n", "labels": {"reads": [{"table": "genetic.projects", "columns": ["genre", "feature_id", "datetime_detention_start", "is_unionized"]}], "writes": [{"table": "organization_contact_individuals", "columns": ["genre", "feature_id", "datetime_detention_start", "is_unionized"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ssn > 450).all()\n# src table: tencel_sources\nengine.execute(\"INSERT INTO dorm SELECT * FROM tencel_sources\")\n", "labels": {"reads": [{"table": "tencel_sources", "columns": null}], "writes": [{"table": "dorm", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO intelligence_personnel SELECT machine_id, union_id, cust_name, life_expectancy FROM constructorstandings WHERE machine_id > 71\"\n", "labels": {"reads": [{"table": "constructorstandings", "columns": ["machine_id", "union_id", "cust_name", "life_expectancy"]}], "writes": [{"table": "intelligence_personnel", "columns": ["machine_id", "union_id", "cust_name", "life_expectancy"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO funding_rounds (snatch, device_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "funding_rounds", "columns": ["snatch", "device_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO voyages SELECT * FROM legacy\ncur.execute(\"SELECT game_name, num_cases FROM tb_reports LIMIT 78\")\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["game_name", "num_cases"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model refugees depends on military_bases\ndbt build --select refugees --vars 'source: military_bases'\n", "labels": {"reads": [{"table": "military_bases", "columns": null}], "writes": [{"table": "refugees", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"assets\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "assets", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO candidates SELECT transaction_category, date_assigned_to FROM gamedata WHERE transaction_category > 339\")\n", "labels": {"reads": [{"table": "gamedata", "columns": ["transaction_category", "date_assigned_to"]}], "writes": [{"table": "candidates", "columns": ["transaction_category", "date_assigned_to"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO operation SELECT * FROM legacy\ncur.execute(\"SELECT recorded_by_staff_id, game_count FROM mart.coupon_use_hourly LIMIT 332\")\n", "labels": {"reads": [{"table": "mart.coupon_use_hourly", "columns": ["recorded_by_staff_id", "game_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 461;\nEOF\n", "labels": {"reads": [{"table": "cinema", "columns": ["card_number", "arrival_time"]}], "writes": [{"table": "food_production", "columns": ["card_number", "arrival_time"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cybersecuritybudget\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"instructors\")\n", "labels": {"reads": [{"table": "cybersecuritybudget", "columns": null}], "writes": [{"table": "instructors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"injury_accident\").toPandas()\ndf[[\"unsure_rate\", \"spacecraft_name\"]].to_sql(\"communitypolicing\", engine, index=False)\n", "labels": {"reads": [{"table": "injury_accident", "columns": null}], "writes": [{"table": "communitypolicing", "columns": ["unsure_rate", "spacecraft_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO benefits_overpayments SELECT artifactid, valuation, artwork_name, charging_level FROM underwater_cables WHERE artifactid > 63\")\n", "labels": {"reads": [{"table": "underwater_cables", "columns": ["artifactid", "valuation", "artwork_name", "charging_level"]}], "writes": [{"table": "benefits_overpayments", "columns": ["artifactid", "valuation", "artwork_name", "charging_level"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO wildlife_sanctuaries SELECT grade, co2_reduction_tons, electoral_register_id FROM behavior_incident WHERE grade > 128\"\n", "labels": {"reads": [{"table": "behavior_incident", "columns": ["grade", "co2_reduction_tons", "electoral_register_id"]}], "writes": [{"table": "wildlife_sanctuaries", "columns": ["grade", "co2_reduction_tons", "electoral_register_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO artsales SELECT vulnerability_name, release_date, diagnosis FROM malicious_activity WHERE vulnerability_name > 323\")\n", "labels": {"reads": [{"table": "malicious_activity", "columns": ["vulnerability_name", "release_date", "diagnosis"]}], "writes": [{"table": "artsales", "columns": ["vulnerability_name", "release_date", "diagnosis"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"apartments\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"measurement\")\n", "labels": {"reads": [{"table": "apartments", "columns": null}], "writes": [{"table": "measurement", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM shared_rides_tokyo\", conn)\ndf.to_sql(\"workforce_training\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "shared_rides_tokyo", "columns": null}], "writes": [{"table": "workforce_training", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO customer_policies SELECT a.productionrate, b.commodity FROM lead_mines a JOIN solar_plants b ON a.max_gust_speed_mph = b.max_gust_speed_mph\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "lead_mines", "columns": null}, {"table": "solar_plants", "columns": null}], "writes": [{"table": "customer_policies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT neighborhoodid, part_fault_id FROM climate_projects LIMIT 369\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "climate_projects", "columns": ["neighborhoodid", "part_fault_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table brand_info --columns author_community,date_of_attendance --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "brand_info", "columns": ["author_community", "date_of_attendance"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO call_volume SELECT investor_details, organized_by, precedent_id, vendor_id FROM supportprograms WHERE investor_details > 30\")\n", "labels": {"reads": [{"table": "supportprograms", "columns": ["investor_details", "organized_by", "precedent_id", "vendor_id"]}], "writes": [{"table": "call_volume", "columns": ["investor_details", "organized_by", "precedent_id", "vendor_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.fueldate > 36).all()\n# src table: clothingsales\nengine.execute(\"INSERT INTO all_documents SELECT * FROM clothingsales\")\n", "labels": {"reads": [{"table": "clothingsales", "columns": null}], "writes": [{"table": "all_documents", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"browser\").toPandas()\ndf[[\"platform\", \"outcome_date\"]].to_sql(\"sponsorship_donations\", engine, index=False)\n", "labels": {"reads": [{"table": "browser", "columns": null}], "writes": [{"table": "sponsorship_donations", "columns": ["platform", "outcome_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT claim_status_description, max_temperature_f FROM dwd_payments_delta LIMIT 229\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "dwd_payments_delta", "columns": ["claim_status_description", "max_temperature_f"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 83;\nSQL\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": ["book_id", "complete_date"]}, {"table": "hospitals", "columns": ["reader_id", "preferred_foot", "destination_name", "wins"]}], "writes": [{"table": "smart_contracts_table", "columns": ["reader_id", "preferred_foot", "destination_name", "wins"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"recyclingcenters\");\ndf.write().mode(\"overwrite\").saveAsTable(\"military_technology_projects\");\n", "labels": {"reads": [{"table": "recyclingcenters", "columns": null}], "writes": [{"table": "military_technology_projects", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO crop_temperature SELECT address_id, fleet_series, concertid, siteid FROM smart_contracts_table WHERE address_id > 104\"\n", "labels": {"reads": [{"table": "smart_contracts_table", "columns": ["address_id", "fleet_series", "concertid", "siteid"]}], "writes": [{"table": "crop_temperature", "columns": ["address_id", "fleet_series", "concertid", "siteid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO vehicle_sales SELECT rural, inspection_date FROM cases WHERE rural > 310\");\n", "labels": {"reads": [{"table": "cases", "columns": ["rural", "inspection_date"]}], "writes": [{"table": "vehicle_sales", "columns": ["rural", "inspection_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"songs_length\").where(\"dt = current_date()\").writeTo(\"cybersecurityincidents\").append()\n", "labels": {"reads": [{"table": "songs_length", "columns": null}], "writes": [{"table": "cybersecurityincidents", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT invoice_date, steps FROM head LIMIT 453\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO ads.events SELECT site_name, focus FROM office_locations WHERE site_name > 473\")\n", "labels": {"reads": [{"table": "head", "columns": ["invoice_date", "steps"]}, {"table": "office_locations", "columns": ["site_name", "focus"]}], "writes": [{"table": "ads.events", "columns": ["site_name", "focus"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd.dwd_risk_score_delta (strategy, grant_end_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": ["strategy", "grant_end_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"dailyapplestreams\")\npush_to_target(df, \"cargo_equipment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dailyapplestreams", "columns": null}], "writes": [{"table": "cargo_equipment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fishcaught\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"infrastructureprojects\")\n", "labels": {"reads": [{"table": "fishcaught", "columns": null}], "writes": [{"table": "infrastructureprojects", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO web_client_accelerator SELECT contractor_name, vehicletype, year_opened, founder_gender FROM workplace_safety WHERE contractor_name > 113\")\n", "labels": {"reads": [{"table": "workplace_safety", "columns": ["contractor_name", "vehicletype", "year_opened", "founder_gender"]}], "writes": [{"table": "web_client_accelerator", "columns": ["contractor_name", "vehicletype", "year_opened", "founder_gender"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO vehicle_registrations SELECT has_disability, port_code, wage_increase, vendor_name FROM product_details WHERE has_disability > 118\"\n", "labels": {"reads": [{"table": "product_details", "columns": ["has_disability", "port_code", "wage_increase", "vendor_name"]}], "writes": [{"table": "vehicle_registrations", "columns": ["has_disability", "port_code", "wage_increase", "vendor_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM programs\", conn)\ndf.to_sql(\"lessons\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "programs", "columns": null}], "writes": [{"table": "lessons", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO cargo_tracking SELECT collection_id, supportrepid FROM employeedemographics WHERE collection_id > 90\")\n", "labels": {"reads": [{"table": "employeedemographics", "columns": ["collection_id", "supportrepid"]}], "writes": [{"table": "cargo_tracking", "columns": ["collection_id", "supportrepid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table department_publications --columns src_apid,clean_jerk --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "department_publications", "columns": ["src_apid", "clean_jerk"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT image_date, venue FROM medals\", engine)\nimport logging\ndf.to_sql(\"spending\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "medals", "columns": ["image_date", "venue"]}], "writes": [{"table": "spending", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO product_details SELECT volunteer_id, organization, heritage_site FROM spacecraft_components WHERE volunteer_id > 99\")\n", "labels": {"reads": [{"table": "spacecraft_components", "columns": ["volunteer_id", "organization", "heritage_site"]}], "writes": [{"table": "product_details", "columns": ["volunteer_id", "organization", "heritage_site"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"vr_adopters\")\nsrc.write.insertInto(\"order_items\", overwrite=True)\n", "labels": {"reads": [{"table": "vr_adopters", "columns": null}], "writes": [{"table": "order_items", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM online_travel_agency\", conn)\ndf.to_sql(\"clothingitems\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "online_travel_agency", "columns": null}], "writes": [{"table": "clothingitems", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"container\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "container", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT trade, investment_date FROM electric_taxis\", engine)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"sites_me\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "electric_taxis", "columns": ["trade", "investment_date"]}], "writes": [{"table": "sites_me", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model movie depends on concert_sales\ndbt run -s movie --vars '{\"src\":\"concert_sales\"}'\n", "labels": {"reads": [{"table": "concert_sales", "columns": null}], "writes": [{"table": "movie", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"therapists\").toPandas()\ndf[[\"vin\", \"site_name\"]].to_sql(\"hair_care_sales\", engine, index=False)\n", "labels": {"reads": [{"table": "therapists", "columns": null}], "writes": [{"table": "hair_care_sales", "columns": ["vin", "site_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.ods_campaigns_delta\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"district_schools\")\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": null}], "writes": [{"table": "district_schools", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO grants SELECT a.investment, b.archeologist FROM factory_water a JOIN user_workouts_march b ON a.registration_id = b.registration_id\"\n", "labels": {"reads": [{"table": "factory_water", "columns": null}, {"table": "user_workouts_march", "columns": null}], "writes": [{"table": "grants", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.refunds_hourly SELECT planned_delivery_date, singer_id, digital, time_second FROM awards WHERE planned_delivery_date > 213\"], check=True)\n", "labels": {"reads": [{"table": "awards", "columns": ["planned_delivery_date", "singer_id", "digital", "time_second"]}], "writes": [{"table": "stg.refunds_hourly", "columns": ["planned_delivery_date", "singer_id", "digital", "time_second"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model user_workouts_march depends on skincaresales\ndbt build --models user_workouts_march --vars 'source: skincaresales'\n", "labels": {"reads": [{"table": "skincaresales", "columns": null}], "writes": [{"table": "user_workouts_march", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"cybersecurityincidents\")\nsrc.write.insertInto(\"climate_data\", overwrite=True)\n", "labels": {"reads": [{"table": "cybersecurityincidents", "columns": null}], "writes": [{"table": "climate_data", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO pollutionincidents SELECT * FROM legacy\nspark.sql(\"INSERT INTO league_x SELECT crime_date, gradepoint FROM rental WHERE crime_date > 493\")\n", "labels": {"reads": [{"table": "rental", "columns": ["crime_date", "gradepoint"]}], "writes": [{"table": "league_x", "columns": ["crime_date", "gradepoint"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"product_catalog\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bi.coupon_use\")\n", "labels": {"reads": [{"table": "product_catalog", "columns": null}], "writes": [{"table": "bi.coupon_use", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO training (fish_id, round_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "training", "columns": ["fish_id", "round_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"healthequitymetrics\");\ndf.write().mode(\"overwrite\").saveAsTable(\"public.ev_sales\");\n", "labels": {"reads": [{"table": "healthequitymetrics", "columns": null}], "writes": [{"table": "public.ev_sales", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model carbonoffsetinitiatives depends on upgrades\ndbt build --models carbonoffsetinitiatives --vars '{\"src\":\"upgrades\"}'\n", "labels": {"reads": [{"table": "upgrades", "columns": null}], "writes": [{"table": "carbonoffsetinitiatives", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_risk_score_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"public.ev_sales\")\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": null}], "writes": [{"table": "public.ev_sales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM conservation_projects\", conn)\ndf.to_sql(\"authors\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "conservation_projects", "columns": null}], "writes": [{"table": "authors", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.bank_id > 258).all()\n# src table: excavation_sites\nengine.execute(\"INSERT INTO stg.coupon_use SELECT * FROM excavation_sites\")\n", "labels": {"reads": [{"table": "excavation_sites", "columns": null}], "writes": [{"table": "stg.coupon_use", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart_shipments_full SELECT ethnicity, fueldate FROM basketball_match WHERE ethnicity > 248\"\n", "labels": {"reads": [{"table": "basketball_match", "columns": ["ethnicity", "fueldate"]}], "writes": [{"table": "mart_shipments_full", "columns": ["ethnicity", "fueldate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"playergamehistory\").where(\"dt = current_date()\").writeTo(\"catalog_structure\").append()\n", "labels": {"reads": [{"table": "playergamehistory", "columns": null}], "writes": [{"table": "catalog_structure", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 138;\nEOF\n", "labels": {"reads": [{"table": "item_inventory", "columns": ["followers", "eco_certified"]}], "writes": [{"table": "health_equity_metrics", "columns": ["followers", "eco_certified"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO immunization SELECT student_details, component_type FROM ref_locations WHERE student_details > 375\");\n", "labels": {"reads": [{"table": "ref_locations", "columns": ["student_details", "component_type"]}], "writes": [{"table": "immunization", "columns": ["student_details", "component_type"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO restorative_justice_center (update_date, item_sold) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "restorative_justice_center", "columns": ["update_date", "item_sold"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO offender_demographics SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bike_station_info\");\ndf.write().mode(\"overwrite\").saveAsTable(\"equipment_maintenance\");\n", "labels": {"reads": [{"table": "bike_station_info", "columns": null}], "writes": [{"table": "equipment_maintenance", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"geological_survey\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "geological_survey", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO business_rates (time_id, granteeid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "business_rates", "columns": ["time_id", "granteeid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"container_ships\")\nupsert_to_store(df, \"research_grants\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "container_ships", "columns": null}], "writes": [{"table": "research_grants", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT international_passengers, exhibitionname FROM satisfaction LIMIT 36\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "satisfaction", "columns": ["international_passengers", "exhibitionname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mobile_plans\").toPandas()\ndf[[\"impressions\", \"productiondate\"]].to_sql(\"ods.clicks_delta\", engine, index=False)\n", "labels": {"reads": [{"table": "mobile_plans", "columns": null}], "writes": [{"table": "ods.clicks_delta", "columns": ["impressions", "productiondate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO hires SELECT framework_name, campaign_name FROM fields WHERE framework_name > 154\")\n", "labels": {"reads": [{"table": "fields", "columns": ["framework_name", "campaign_name"]}], "writes": [{"table": "hires", "columns": ["framework_name", "campaign_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT height, section_id FROM australian_states LIMIT 450\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO defense_diplomacy SELECT inclusive, supplier_id FROM customer WHERE inclusive > 14\")\n", "labels": {"reads": [{"table": "australian_states", "columns": ["height", "section_id"]}, {"table": "customer", "columns": ["inclusive", "supplier_id"]}], "writes": [{"table": "defense_diplomacy", "columns": ["inclusive", "supplier_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"livestock\")\npersist_to_target(df, \"purchases\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "livestock", "columns": null}], "writes": [{"table": "purchases", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"green_certification\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"electric_vehicle_stats\")\n", "labels": {"reads": [{"table": "green_certification", "columns": null}], "writes": [{"table": "electric_vehicle_stats", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"mart_orders_di\")\npush_to_warehouse(df, \"species_forests\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart_orders_di", "columns": null}], "writes": [{"table": "species_forests", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO user_likes SELECT trial_success_rate, fare_id FROM tryout WHERE trial_success_rate > 466\")\n", "labels": {"reads": [{"table": "tryout", "columns": ["trial_success_rate", "fare_id"]}], "writes": [{"table": "user_likes", "columns": ["trial_success_rate", "fare_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stg.stg_inventory_full (max_salary, spent) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_inventory_full", "columns": ["max_salary", "spent"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table platformi --columns vehicle_type,vendor_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "platformi", "columns": ["vehicle_type", "vendor_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 333;\nSQL\n", "labels": {"reads": [{"table": "healthydelights", "columns": ["draft_pick_number", "warehousename"]}, {"table": "cargo_data", "columns": ["meal_id", "clean_jerk", "left_office"]}], "writes": [{"table": "recreation_centers", "columns": ["meal_id", "clean_jerk", "left_office"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM subjects\", conn)\ndf.to_sql(\"tournaments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "subjects", "columns": null}], "writes": [{"table": "tournaments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO roller_coaster SELECT home_games, resolutiondate, policy_type_code FROM biosensor.patents WHERE home_games > 249\");\n", "labels": {"reads": [{"table": "biosensor.patents", "columns": ["home_games", "resolutiondate", "policy_type_code"]}], "writes": [{"table": "roller_coaster", "columns": ["home_games", "resolutiondate", "policy_type_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"stg.stg_events_di\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model cases depends on esports_teams\ndbt run --models cases --vars '{\"source_table\":\"esports_teams\"}'\n", "labels": {"reads": [{"table": "esports_teams", "columns": null}], "writes": [{"table": "cases", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"complaints\")\ndump_to_store(df, \"representative\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "complaints", "columns": null}], "writes": [{"table": "representative", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT releasedate, policyholder_id FROM stg.stg_risk_score_df LIMIT 11\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO vocals SELECT animal_id, program_date, image_url FROM comments WHERE animal_id > 93\")\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": ["releasedate", "policyholder_id"]}, {"table": "comments", "columns": ["animal_id", "program_date", "image_url"]}], "writes": [{"table": "vocals", "columns": ["animal_id", "program_date", "image_url"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mental_health_center\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"vessel\")\n", "labels": {"reads": [{"table": "mental_health_center", "columns": null}], "writes": [{"table": "vessel", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM sustainable_menu_items\"\n", "labels": {"reads": [{"table": "sustainable_menu_items", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT fundingagency, movieid FROM veteran_occupations LIMIT 376\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO stats SELECT ip_address, contactid, allergytype, network FROM attorneylocationyear WHERE ip_address > 221\")\n", "labels": {"reads": [{"table": "veteran_occupations", "columns": ["fundingagency", "movieid"]}, {"table": "attorneylocationyear", "columns": ["ip_address", "contactid", "allergytype", "network"]}], "writes": [{"table": "stats", "columns": ["ip_address", "contactid", "allergytype", "network"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 274;\nEOF\n", "labels": {"reads": [{"table": "stateinfrastructure", "columns": ["facility_code", "end_time", "profits_in_billion", "ei_value"]}], "writes": [{"table": "employee_demographics", "columns": ["facility_code", "end_time", "profits_in_billion", "ei_value"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO sites_me SELECT population, satellite, operationtype, date_of_publication FROM incarcerated WHERE population > 64\"\n", "labels": {"reads": [{"table": "incarcerated", "columns": ["population", "satellite", "operationtype", "date_of_publication"]}], "writes": [{"table": "sites_me", "columns": ["population", "satellite", "operationtype", "date_of_publication"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"school_roster\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"humanitarian_operations\")\n", "labels": {"reads": [{"table": "school_roster", "columns": null}], "writes": [{"table": "humanitarian_operations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi.bi_orders_hourly (communitytype, trial_success_rate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_orders_hourly", "columns": ["communitytype", "trial_success_rate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO patient_satisfaction SELECT local_authority, race, spf_level, certification FROM clinic_2022 WHERE local_authority > 412\"\n", "labels": {"reads": [{"table": "clinic_2022", "columns": ["local_authority", "race", "spf_level", "certification"]}], "writes": [{"table": "patient_satisfaction", "columns": ["local_authority", "race", "spf_level", "certification"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"hotel_tech_adoption\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "hotel_tech_adoption", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cb_agreements\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"product_sales\")\n", "labels": {"reads": [{"table": "cb_agreements", "columns": null}], "writes": [{"table": "product_sales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO project_timelines SELECT order_status, timestamp FROM communityengagementmetrics WHERE order_status > 100\")\n", "labels": {"reads": [{"table": "communityengagementmetrics", "columns": ["order_status", "timestamp"]}], "writes": [{"table": "project_timelines", "columns": ["order_status", "timestamp"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"wind_projects\")\npersist_to_target(df, \"policy\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "wind_projects", "columns": null}], "writes": [{"table": "policy", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"soccer_teams\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws_shipments_df\")\n", "labels": {"reads": [{"table": "soccer_teams", "columns": null}], "writes": [{"table": "dws_shipments_df", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO manufacturersustainability SELECT team_id_br, request_date FROM residents_services WHERE team_id_br > 414\"\n", "labels": {"reads": [{"table": "residents_services", "columns": ["team_id_br", "request_date"]}], "writes": [{"table": "manufacturersustainability", "columns": ["team_id_br", "request_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"exhibitiondetails\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO articles SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO bi.risk_score_df SELECT electoral_register_id, pixels, number_of_sightings FROM students_lifelong_learning WHERE electoral_register_id > 182\");\n", "labels": {"reads": [{"table": "students_lifelong_learning", "columns": ["electoral_register_id", "pixels", "number_of_sightings"]}], "writes": [{"table": "bi.risk_score_df", "columns": ["electoral_register_id", "pixels", "number_of_sightings"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO ocean_species SELECT averagespeed, taskid, date_of_ceremony, primary_advisor FROM buildings WHERE averagespeed > 403\")\n", "labels": {"reads": [{"table": "buildings", "columns": ["averagespeed", "taskid", "date_of_ceremony", "primary_advisor"]}], "writes": [{"table": "ocean_species", "columns": ["averagespeed", "taskid", "date_of_ceremony", "primary_advisor"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO retailerg SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO mart.device_log_hourly SELECT reo_type, matchdate, actor_name, aid FROM historicalcontexts WHERE reo_type > 401\")\n", "labels": {"reads": [{"table": "historicalcontexts", "columns": ["reo_type", "matchdate", "actor_name", "aid"]}], "writes": [{"table": "mart.device_log_hourly", "columns": ["reo_type", "matchdate", "actor_name", "aid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"cultural_events\").where(\"dt = current_date()\").writeTo(\"chemicalproducts\").append()\n", "labels": {"reads": [{"table": "cultural_events", "columns": null}], "writes": [{"table": "chemicalproducts", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table stg_orders_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg_orders_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ethics_violations SELECT building_name, categoryname, volunteer_id, school FROM player WHERE building_name > 75\"\n", "labels": {"reads": [{"table": "player", "columns": ["building_name", "categoryname", "volunteer_id", "school"]}], "writes": [{"table": "ethics_violations", "columns": ["building_name", "categoryname", "volunteer_id", "school"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table safety_incidents_india --columns acc_percent,amenity_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "safety_incidents_india", "columns": ["acc_percent", "amenity_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fare_collection\").toPandas()\ndf[[\"member_in_charge_id\", \"donor_name\"]].to_sql(\"status\", engine, index=False)\n", "labels": {"reads": [{"table": "fare_collection", "columns": null}], "writes": [{"table": "status", "columns": ["member_in_charge_id", "donor_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"causes_insert_2\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "causes_insert_2", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.stg_users_di SELECT max_aperture, menucategory FROM volunteer_hours WHERE max_aperture > 481\"\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": ["max_aperture", "menucategory"]}], "writes": [{"table": "stg.stg_users_di", "columns": ["max_aperture", "menucategory"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"player_college\")\nsrc.write.insertInto(\"movies\", overwrite=True)\n", "labels": {"reads": [{"table": "player_college", "columns": null}], "writes": [{"table": "movies", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table playergamedata --columns headquarters,hours_played --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "playergamedata", "columns": ["headquarters", "hours_played"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dysprosiumproduction\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"gamesales\")\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": null}], "writes": [{"table": "gamesales", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO measurement SELECT a.description, b.draft_details FROM well_production a JOIN products_in_events b ON a.mailing_date = b.mailing_date\"\n", "labels": {"reads": [{"table": "well_production", "columns": null}, {"table": "products_in_events", "columns": null}], "writes": [{"table": "measurement", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.clicks\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"police_emergencies\")\n", "labels": {"reads": [{"table": "mart.clicks", "columns": null}], "writes": [{"table": "police_emergencies", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO refugees (operationname, trial_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "refugees", "columns": ["operationname", "trial_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO projects SELECT spacecraft_model, season, drug_name, founder_race FROM crime_stats WHERE spacecraft_model > 376\"\n", "labels": {"reads": [{"table": "crime_stats", "columns": ["spacecraft_model", "season", "drug_name", "founder_race"]}], "writes": [{"table": "projects", "columns": ["spacecraft_model", "season", "drug_name", "founder_race"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM exit_strategies\", conn)\ndf.to_sql(\"bi.inventory_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "exit_strategies", "columns": null}], "writes": [{"table": "bi.inventory_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO upgrades SELECT inspectionscore, access_date, asset_type, num_pallets FROM africa_schema.african_mines WHERE inspectionscore > 429\")\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["inspectionscore", "access_date", "asset_type", "num_pallets"]}], "writes": [{"table": "upgrades", "columns": ["inspectionscore", "access_date", "asset_type", "num_pallets"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO operate_company SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO conservation_projects SELECT a.membergender, b.cultural_significance FROM property_community a JOIN animal_population_status b ON a.fund_name = b.fund_name\"\n", "labels": {"reads": [{"table": "property_community", "columns": null}, {"table": "animal_population_status", "columns": null}], "writes": [{"table": "conservation_projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"military_technology_projects\");\ndf.write().mode(\"overwrite\").saveAsTable(\"athlete_wellbeing\");\n", "labels": {"reads": [{"table": "military_technology_projects", "columns": null}], "writes": [{"table": "athlete_wellbeing", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"local_impact\");\ndf.write().mode(\"overwrite\").saveAsTable(\"operate_company\");\n", "labels": {"reads": [{"table": "local_impact", "columns": null}], "writes": [{"table": "operate_company", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_adaptation_projects\"\n", "labels": {"reads": [{"table": "climate_adaptation_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mobile_customers_global\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mobile_customers_global", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table state_usage --target-dir /tmp/land\n", "labels": {"reads": [{"table": "state_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO athletes SELECT district_name, violationtype FROM patents WHERE district_name > 191\")\n", "labels": {"reads": [{"table": "patents", "columns": ["district_name", "violationtype"]}], "writes": [{"table": "athletes", "columns": ["district_name", "violationtype"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 139;\nSQL\n", "labels": {"reads": [{"table": "vessel_types", "columns": ["teamname", "bats"]}, {"table": "classrooms", "columns": ["extraction_amount", "facility_name", "donor_country", "drought_id"]}], "writes": [{"table": "spacecrafts", "columns": ["extraction_amount", "facility_name", "donor_country", "drought_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO wastewater_treatment (category_id, cause_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "wastewater_treatment", "columns": ["category_id", "cause_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM militaryinnovations\"\n", "labels": {"reads": [{"table": "militaryinnovations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_tourism_practices\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "sustainable_tourism_practices", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"player_demographics\").toPandas()\ndf[[\"weight\", \"number_of_sightings\"]].to_sql(\"open_pedagogy_courses\", engine, index=False)\n", "labels": {"reads": [{"table": "player_demographics", "columns": null}], "writes": [{"table": "open_pedagogy_courses", "columns": ["weight", "number_of_sightings"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO fleets SELECT * FROM legacy\ncur.execute(\"SELECT hourlyrate, home_team_id FROM locations_oceania LIMIT 186\")\n", "labels": {"reads": [{"table": "locations_oceania", "columns": ["hourlyrate", "home_team_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO campuses SELECT eco_friendly, farmid FROM bookings WHERE eco_friendly > 475\"], check=True)\n", "labels": {"reads": [{"table": "bookings", "columns": ["eco_friendly", "farmid"]}], "writes": [{"table": "campuses", "columns": ["eco_friendly", "farmid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT quantitysold, passengers FROM cinema LIMIT 137\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "cinema", "columns": ["quantitysold", "passengers"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO housing_investments (enable_dm, emergency_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "housing_investments", "columns": ["enable_dm", "emergency_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO shipmentinfo SELECT coal_reserve_remaining, impactid, treatment, stu_num FROM people WHERE coal_reserve_remaining > 389\");\n", "labels": {"reads": [{"table": "people", "columns": ["coal_reserve_remaining", "impactid", "treatment", "stu_num"]}], "writes": [{"table": "shipmentinfo", "columns": ["coal_reserve_remaining", "impactid", "treatment", "stu_num"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.ods_sessions_df\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"fishcaught\")\n", "labels": {"reads": [{"table": "ods.ods_sessions_df", "columns": null}], "writes": [{"table": "fishcaught", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO continent (num_transactions, requestid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "continent", "columns": ["num_transactions", "requestid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO dw.dw_sessions_delta SELECT num_of_component, unique_founders FROM comments WHERE num_of_component > 299\")\n", "labels": {"reads": [{"table": "comments", "columns": ["num_of_component", "unique_founders"]}], "writes": [{"table": "dw.dw_sessions_delta", "columns": ["num_of_component", "unique_founders"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"rural_economy_2\")\nsrc.write.insertInto(\"state_info\", overwrite=True)\n", "labels": {"reads": [{"table": "rural_economy_2", "columns": null}], "writes": [{"table": "state_info", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"labor_cost\")\nwrite_to_output(df, \"threat_intel\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "labor_cost", "columns": null}], "writes": [{"table": "threat_intel", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ods.ods_events_daily SELECT reviews, treatment_date FROM europium_exports WHERE reviews > 257\"\n", "labels": {"reads": [{"table": "europium_exports", "columns": ["reviews", "treatment_date"]}], "writes": [{"table": "ods.ods_events_daily", "columns": ["reviews", "treatment_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO police_emergencies (drought_id, range) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "police_emergencies", "columns": ["drought_id", "range"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"sustainable_menu_items\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "sustainable_menu_items", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"seamounts\")\nsave_to_store(df, \"research_grants\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "seamounts", "columns": null}], "writes": [{"table": "research_grants", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.mart_vendors SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"prescribes\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "prescribes", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO gameattendance SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ethicalaibudget\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"elimination\")\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": [{"table": "elimination", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"creative_ai\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "creative_ai", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fuel_consumption\", conn)\ndf.to_sql(\"film_category\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fuel_consumption", "columns": null}], "writes": [{"table": "film_category", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table innovation_trends --columns drugname,interaction_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "innovation_trends", "columns": ["drugname", "interaction_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO passengers SELECT therapeutic_area, rating_id, energy_efficiency_rating, longitude FROM mars_spacecraft WHERE therapeutic_area > 25\"\n", "labels": {"reads": [{"table": "mars_spacecraft", "columns": ["therapeutic_area", "rating_id", "energy_efficiency_rating", "longitude"]}], "writes": [{"table": "passengers", "columns": ["therapeutic_area", "rating_id", "energy_efficiency_rating", "longitude"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ads.ads_payments\").where(\"dt = current_date()\").writeTo(\"hockey_players\").append()\n", "labels": {"reads": [{"table": "ads.ads_payments", "columns": null}], "writes": [{"table": "hockey_players", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.products_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods.products_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table garmentproduction --columns report,organization_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "garmentproduction", "columns": ["report", "organization_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vessels_2 SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"haircare_cruelty\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "haircare_cruelty", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO savings_programs SELECT is_commercial, quantitysold FROM climate_mitigation_projects WHERE is_commercial > 243\")\n", "labels": {"reads": [{"table": "climate_mitigation_projects", "columns": ["is_commercial", "quantitysold"]}], "writes": [{"table": "savings_programs", "columns": ["is_commercial", "quantitysold"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO mars_rovers SELECT altitude, bookings, build_year, trainingdate FROM mart.mart_member_point_hourly WHERE altitude > 144\")\n", "labels": {"reads": [{"table": "mart.mart_member_point_hourly", "columns": ["altitude", "bookings", "build_year", "trainingdate"]}], "writes": [{"table": "mars_rovers", "columns": ["altitude", "bookings", "build_year", "trainingdate"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO safetyincidents SELECT image_data, ironid, facultyid, nation FROM dependent WHERE image_data > 53\")\n", "labels": {"reads": [{"table": "dependent", "columns": ["image_data", "ironid", "facultyid", "nation"]}], "writes": [{"table": "safetyincidents", "columns": ["image_data", "ironid", "facultyid", "nation"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ads.ads_device_log_di\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"races\")\n", "labels": {"reads": [{"table": "ads.ads_device_log_di", "columns": null}], "writes": [{"table": "races", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"campaigns_2023\")\nupsert_to_output(df, \"satellite_deployment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "campaigns_2023", "columns": null}], "writes": [{"table": "satellite_deployment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"heart_rate_data\");\ndf.write().mode(\"overwrite\").saveAsTable(\"department\");\n", "labels": {"reads": [{"table": "heart_rate_data", "columns": null}], "writes": [{"table": "department", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO hair_care_sales SELECT menuid, fish_count, evaluated_for_fairness FROM licenses WHERE menuid > 263\")\n", "labels": {"reads": [{"table": "licenses", "columns": ["menuid", "fish_count", "evaluated_for_fairness"]}], "writes": [{"table": "hair_care_sales", "columns": ["menuid", "fish_count", "evaluated_for_fairness"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"pilot_record\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"model_data\")\n", "labels": {"reads": [{"table": "pilot_record", "columns": null}], "writes": [{"table": "model_data", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"takes\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"stateinfrastructure\")\n", "labels": {"reads": [{"table": "takes", "columns": null}], "writes": [{"table": "stateinfrastructure", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.co2_offset_amount > 500).all()\n# src table: dwd.coupon_use_daily\nengine.execute(\"INSERT INTO ocean_species SELECT * FROM dwd.coupon_use_daily\")\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": null}], "writes": [{"table": "ocean_species", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ancient_artifacts SELECT * FROM legacy\ncur.execute(\"SELECT taxi_model, catalog_entry_id FROM areas LIMIT 472\")\n", "labels": {"reads": [{"table": "areas", "columns": ["taxi_model", "catalog_entry_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT visitorid, cancel_date FROM securityincidents LIMIT 3\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "securityincidents", "columns": ["visitorid", "cancel_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO thefts SELECT membername, dec FROM charging_stations WHERE membername > 58\")\n", "labels": {"reads": [{"table": "charging_stations", "columns": ["membername", "dec"]}], "writes": [{"table": "thefts", "columns": ["membername", "dec"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.stg_risk_score SELECT hardware_colours, feedtype FROM courts WHERE hardware_colours > 72\"\n", "labels": {"reads": [{"table": "courts", "columns": ["hardware_colours", "feedtype"]}], "writes": [{"table": "stg.stg_risk_score", "columns": ["hardware_colours", "feedtype"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mining_operation (warehouse_name, town_city) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mining_operation", "columns": ["warehouse_name", "town_city"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 3;\nEOF\n", "labels": {"reads": [{"table": "ais", "columns": ["sales_transaction_id", "all_home", "winery", "ethical_certifications"]}], "writes": [{"table": "eventdates", "columns": ["sales_transaction_id", "all_home", "winery", "ethical_certifications"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 344;\nEOF\n", "labels": {"reads": [{"table": "taxi_data", "columns": ["workout_name", "company_type_code", "snatch"]}], "writes": [{"table": "laborstatistics", "columns": ["workout_name", "company_type_code", "snatch"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"storage\").where(\"dt = current_date()\").writeTo(\"fabricinventory\").append()\n", "labels": {"reads": [{"table": "storage", "columns": null}], "writes": [{"table": "fabricinventory", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 104;\nSQL\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": ["date_incident_end", "creationyear"]}, {"table": "dwd.vendors", "columns": ["communityname", "spent", "thefttypeid"]}], "writes": [{"table": "carbon_offsets", "columns": ["communityname", "spent", "thefttypeid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO rural_clinics (sales_details, squadron) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "rural_clinics", "columns": ["sales_details", "squadron"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO course SELECT unsure_rate, scooter_id, active FROM spacecraft_manufacturers WHERE unsure_rate > 191\"], check=True)\n", "labels": {"reads": [{"table": "spacecraft_manufacturers", "columns": ["unsure_rate", "scooter_id", "active"]}], "writes": [{"table": "course", "columns": ["unsure_rate", "scooter_id", "active"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_sessions_df\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi.bi_sessions_df", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT trip_end_time, transactions FROM textileworkers LIMIT 34\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "textileworkers", "columns": ["trip_end_time", "transactions"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"strandings\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "strandings", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO defensespending SELECT stu_fname, is_accessible FROM arctic_research WHERE stu_fname > 254\")\n", "labels": {"reads": [{"table": "arctic_research", "columns": ["stu_fname", "is_accessible"]}], "writes": [{"table": "defensespending", "columns": ["stu_fname", "is_accessible"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM shariah_compliant_loans\", conn)\ndf.to_sql(\"ads.ads_payments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "shariah_compliant_loans", "columns": null}], "writes": [{"table": "ads.ads_payments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table community_programs --columns donation_id,age_group_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "community_programs", "columns": ["donation_id", "age_group_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT delivery_time, call_count FROM drivers\", engine)\nimport logging\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"onlineengagement\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "drivers", "columns": ["delivery_time", "call_count"]}], "writes": [{"table": "onlineengagement", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table disabilityadvocacy --target-dir /tmp/land\n", "labels": {"reads": [{"table": "disabilityadvocacy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dwd.campaigns\").where(\"dt = current_date()\").writeTo(\"livestock\").append()\n", "labels": {"reads": [{"table": "dwd.campaigns", "columns": null}], "writes": [{"table": "livestock", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO evidence_based_policies SELECT clubid, credits FROM invoice_lines WHERE clubid > 448\"], check=True)\n", "labels": {"reads": [{"table": "invoice_lines", "columns": ["clubid", "credits"]}], "writes": [{"table": "evidence_based_policies", "columns": ["clubid", "credits"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 474;\nSQL\n", "labels": {"reads": [{"table": "donation", "columns": ["resource_type", "restorative_justice"]}, {"table": "researchpapers", "columns": ["student", "destroyed_by_employee_id", "area_id", "start_speed"]}], "writes": [{"table": "volunteer_hours", "columns": ["student", "destroyed_by_employee_id", "area_id", "start_speed"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"game_sales\").toPandas()\ndf[[\"departmentid\", \"visitors\"]].to_sql(\"shoes\", engine, index=False)\n", "labels": {"reads": [{"table": "game_sales", "columns": null}], "writes": [{"table": "shoes", "columns": ["departmentid", "visitors"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 344;\nEOF\n", "labels": {"reads": [{"table": "genre_songs", "columns": ["shipment_date", "vessel_name"]}], "writes": [{"table": "draft_copies", "columns": ["shipment_date", "vessel_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO fashion_trend_data SELECT reports_to, mhw_id, refugee_name FROM pollution_control_initiatives WHERE reports_to > 195\"\n", "labels": {"reads": [{"table": "pollution_control_initiatives", "columns": ["reports_to", "mhw_id", "refugee_name"]}], "writes": [{"table": "fashion_trend_data", "columns": ["reports_to", "mhw_id", "refugee_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"constructionlaborstatistics\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "constructionlaborstatistics", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO livestock SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"hospital\").where(\"dt = current_date()\").writeTo(\"mineral_extraction_us\").append()\n", "labels": {"reads": [{"table": "hospital", "columns": null}], "writes": [{"table": "mineral_extraction_us", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO party_services SELECT sea, claim_id FROM design_standards WHERE sea > 385\");\n", "labels": {"reads": [{"table": "design_standards", "columns": ["sea", "claim_id"]}], "writes": [{"table": "party_services", "columns": ["sea", "claim_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO org_climate_finance (protein_name, line_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "org_climate_finance", "columns": ["protein_name", "line_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO safetytestingcounts SELECT hospitalid, date_payment_made, review_text FROM bi.bi_events_full WHERE hospitalid > 485\"\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": ["hospitalid", "date_payment_made", "review_text"]}], "writes": [{"table": "safetytestingcounts", "columns": ["hospitalid", "date_payment_made", "review_text"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"mart.mart_coupon_use_df\")\npersist_to_target(df, \"certificate\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_df", "columns": null}], "writes": [{"table": "certificate", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO tryout (player, therapy_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "tryout", "columns": ["player", "therapy_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 420;\nEOF\n", "labels": {"reads": [{"table": "opendatainitiatives", "columns": ["score", "lieutenant_governor"]}], "writes": [{"table": "customersregion", "columns": ["score", "lieutenant_governor"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO producersnewmexico SELECT cargo_id, booked_count, assessmentname, exhibitions FROM intelligence_personnel WHERE cargo_id > 182\")\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": ["cargo_id", "booked_count", "assessmentname", "exhibitions"]}], "writes": [{"table": "producersnewmexico", "columns": ["cargo_id", "booked_count", "assessmentname", "exhibitions"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO culturalpractices SELECT carrierid, permits_issued FROM public_transportation_routes WHERE carrierid > 320\");\n", "labels": {"reads": [{"table": "public_transportation_routes", "columns": ["carrierid", "permits_issued"]}], "writes": [{"table": "culturalpractices", "columns": ["carrierid", "permits_issued"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO mart.mart_users_di SELECT a.contributor, b.author FROM dwd.dwd_exposure_df a JOIN concert_events b ON a.dno = b.dno\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_df", "columns": null}, {"table": "concert_events", "columns": null}], "writes": [{"table": "mart.mart_users_di", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"product_review\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "product_review", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.chemical_id > 362).all()\n# src table: wildlife\nengine.execute(\"INSERT INTO geneva_motor_show SELECT * FROM wildlife\")\n", "labels": {"reads": [{"table": "wildlife", "columns": null}], "writes": [{"table": "geneva_motor_show", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO threat_intelligence SELECT journal_id, fund_id, budget, city_id FROM support_groups WHERE journal_id > 223\")\n", "labels": {"reads": [{"table": "support_groups", "columns": ["journal_id", "fund_id", "budget", "city_id"]}], "writes": [{"table": "threat_intelligence", "columns": ["journal_id", "fund_id", "budget", "city_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO intelligence_agency SELECT * FROM legacy\ncur.execute(\"SELECT recycling_rate, num_employees FROM renewableprojects LIMIT 123\")\n", "labels": {"reads": [{"table": "renewableprojects", "columns": ["recycling_rate", "num_employees"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"expenses\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "expenses", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cb_agreements\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.dwd_orders_daily\")\n", "labels": {"reads": [{"table": "cb_agreements", "columns": null}], "writes": [{"table": "dwd.dwd_orders_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 483;\nEOF\n", "labels": {"reads": [{"table": "project_timelines", "columns": ["building_type", "src_apid", "launch_id"]}], "writes": [{"table": "customerorders", "columns": ["building_type", "src_apid", "launch_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"education\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"product_characteristics\")\n", "labels": {"reads": [{"table": "education", "columns": null}], "writes": [{"table": "product_characteristics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model refugee_support depends on eco_diversification_investment\ndbt build -s refugee_support --vars 'source: eco_diversification_investment'\n", "labels": {"reads": [{"table": "eco_diversification_investment", "columns": null}], "writes": [{"table": "refugee_support", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO people_addresses (waste_amount, restorative_justice) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "people_addresses", "columns": ["waste_amount", "restorative_justice"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO product_characteristics SELECT is_commercial, batting_average FROM route WHERE is_commercial > 445\"\n", "labels": {"reads": [{"table": "route", "columns": ["is_commercial", "batting_average"]}], "writes": [{"table": "product_characteristics", "columns": ["is_commercial", "batting_average"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO ads.events SELECT contractor, census_ranking, annual_revenue, detection_id FROM bi.users_df WHERE contractor > 200\");\n", "labels": {"reads": [{"table": "bi.users_df", "columns": ["contractor", "census_ranking", "annual_revenue", "detection_id"]}], "writes": [{"table": "ads.events", "columns": ["contractor", "census_ranking", "annual_revenue", "detection_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO maintenancerequests SELECT soil_moisture, vehicle_details, opid FROM production_sites WHERE soil_moisture > 207\"\n", "labels": {"reads": [{"table": "production_sites", "columns": ["soil_moisture", "vehicle_details", "opid"]}], "writes": [{"table": "maintenancerequests", "columns": ["soil_moisture", "vehicle_details", "opid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cargo, vendorid FROM carbon_footprint LIMIT 48\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO eventdates SELECT budget_type_description, contractor FROM marine_species WHERE budget_type_description > 124\")\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": ["cargo", "vendorid"]}, {"table": "marine_species", "columns": ["budget_type_description", "contractor"]}], "writes": [{"table": "eventdates", "columns": ["budget_type_description", "contractor"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT region, stu_num FROM student_course_registrations LIMIT 239\")\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO local_impact SELECT dish_type, register_year, treatment_date FROM italy_culture WHERE dish_type > 159\")\n", "labels": {"reads": [{"table": "student_course_registrations", "columns": ["region", "stu_num"]}, {"table": "italy_culture", "columns": ["dish_type", "register_year", "treatment_date"]}], "writes": [{"table": "local_impact", "columns": ["dish_type", "register_year", "treatment_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM retail_workers_union\"\n", "labels": {"reads": [{"table": "retail_workers_union", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sustainable_urban_properties_2 SELECT hours_spent, birth_place, fair_labor FROM prices WHERE hours_spent > 497\"\n", "labels": {"reads": [{"table": "prices", "columns": ["hours_spent", "birth_place", "fair_labor"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["hours_spent", "birth_place", "fair_labor"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO carbon_offsets SELECT num_rooms, market_value, value_points FROM recycling_centers WHERE num_rooms > 247\")\n", "labels": {"reads": [{"table": "recycling_centers", "columns": ["num_rooms", "market_value", "value_points"]}], "writes": [{"table": "carbon_offsets", "columns": ["num_rooms", "market_value", "value_points"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ticketspending SELECT a.detention_type_description, b.label_id FROM cybersecurityincidents a JOIN flight_safety b ON a.field_id = b.field_id\"\n", "labels": {"reads": [{"table": "cybersecurityincidents", "columns": null}, {"table": "flight_safety", "columns": null}], "writes": [{"table": "ticketspending", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ods_sessions --columns airline,last_used_bus --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ods_sessions", "columns": ["airline", "last_used_bus"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dw.dw_inventory_df SELECT * FROM legacy\nspark.sql(\"INSERT INTO threat_severity SELECT timestamp, document_id FROM factories_africa WHERE timestamp > 214\")\n", "labels": {"reads": [{"table": "factories_africa", "columns": ["timestamp", "document_id"]}], "writes": [{"table": "threat_severity", "columns": ["timestamp", "document_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.accreditation_level > 58).all()\n# src table: judges\nengine.execute(\"INSERT INTO eu_data_usage SELECT * FROM judges\")\n", "labels": {"reads": [{"table": "judges", "columns": null}], "writes": [{"table": "eu_data_usage", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO student_mental_health SELECT founder_ethnicity, unionname, datetime_detention_start FROM hospital WHERE founder_ethnicity > 326\")\n", "labels": {"reads": [{"table": "hospital", "columns": ["founder_ethnicity", "unionname", "datetime_detention_start"]}], "writes": [{"table": "student_mental_health", "columns": ["founder_ethnicity", "unionname", "datetime_detention_start"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO waste_types SELECT * FROM legacy\nspark.sql(\"INSERT INTO units SELECT trench_id, schedule_id, goal_id, complaint_date FROM artist_info WHERE trench_id > 152\")\n", "labels": {"reads": [{"table": "artist_info", "columns": ["trench_id", "schedule_id", "goal_id", "complaint_date"]}], "writes": [{"table": "units", "columns": ["trench_id", "schedule_id", "goal_id", "complaint_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table broadband_subscribers --columns allergy,mappingname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "broadband_subscribers", "columns": ["allergy", "mappingname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"heart_rate_data\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"port_visits\")\n", "labels": {"reads": [{"table": "heart_rate_data", "columns": null}], "writes": [{"table": "port_visits", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 88;\nSQL\n", "labels": {"reads": [{"table": "mart.vendors_full", "columns": ["case_id", "revenueid"]}, {"table": "community_policing_events", "columns": ["vrgameid", "ai_powered_features", "workeridentity", "county_id"]}], "writes": [{"table": "stories", "columns": ["vrgameid", "ai_powered_features", "workeridentity", "county_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dwd.dwd_coupon_use_df\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ocean_species\")\n", "labels": {"reads": [{"table": "dwd.dwd_coupon_use_df", "columns": null}], "writes": [{"table": "ocean_species", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO daily_articles_by_category SELECT conferencename, budget_allocation FROM circularsupplychain WHERE conferencename > 124\"\n", "labels": {"reads": [{"table": "circularsupplychain", "columns": ["conferencename", "budget_allocation"]}], "writes": [{"table": "daily_articles_by_category", "columns": ["conferencename", "budget_allocation"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO submersible_dives SELECT accommodationtype, contractor_id FROM fair_trade_suppliers WHERE accommodationtype > 55\"\n", "labels": {"reads": [{"table": "fair_trade_suppliers", "columns": ["accommodationtype", "contractor_id"]}], "writes": [{"table": "submersible_dives", "columns": ["accommodationtype", "contractor_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"art_exhibit_attendance\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ads.ads_risk_score_hourly\")\n", "labels": {"reads": [{"table": "art_exhibit_attendance", "columns": null}], "writes": [{"table": "ads.ads_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT characteristic_type_code, treatment_type FROM state_contracts LIMIT 351\")\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO safetytestingcounts SELECT batting_average, tourist_attraction_id, velocity FROM drills WHERE batting_average > 152\")\n", "labels": {"reads": [{"table": "state_contracts", "columns": ["characteristic_type_code", "treatment_type"]}, {"table": "drills", "columns": ["batting_average", "tourist_attraction_id", "velocity"]}], "writes": [{"table": "safetytestingcounts", "columns": ["batting_average", "tourist_attraction_id", "velocity"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO customer SELECT * FROM legacy\ncur.execute(\"SELECT mission_name, date_problem_reported FROM chemical LIMIT 42\")\n", "labels": {"reads": [{"table": "chemical", "columns": ["mission_name", "date_problem_reported"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table coowners --target-dir /tmp/land\n", "labels": {"reads": [{"table": "coowners", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO cybersecurity_incidents SELECT programoutcomeid, funding_received, festival_name, mappingname FROM sustainable_projects WHERE programoutcomeid > 384\");\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["programoutcomeid", "funding_received", "festival_name", "mappingname"]}], "writes": [{"table": "cybersecurity_incidents", "columns": ["programoutcomeid", "funding_received", "festival_name", "mappingname"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO soccer_teams (startyear, last_used_bus) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "soccer_teams", "columns": ["startyear", "last_used_bus"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO funding_rounds SELECT contractorid, meal_id, accessibility FROM equipmentsales WHERE contractorid > 70\"\n", "labels": {"reads": [{"table": "equipmentsales", "columns": ["contractorid", "meal_id", "accessibility"]}], "writes": [{"table": "funding_rounds", "columns": ["contractorid", "meal_id", "accessibility"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO cloud_issues SELECT longitude, vendorid, cultivatorname FROM carbon_footprint WHERE longitude > 485\")\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": ["longitude", "vendorid", "cultivatorname"]}], "writes": [{"table": "cloud_issues", "columns": ["longitude", "vendorid", "cultivatorname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO renewableprojects SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table harvest_permits --columns material,problem_description --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "harvest_permits", "columns": ["material", "problem_description"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"excavation_sites\").toPandas()\ndf[[\"framework_id\", \"biomass\"]].to_sql(\"haircaresales\", engine, index=False)\n", "labels": {"reads": [{"table": "excavation_sites", "columns": null}], "writes": [{"table": "haircaresales", "columns": ["framework_id", "biomass"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO bi.device_log SELECT focus, postal_code FROM artpieces WHERE focus > 83\");\n", "labels": {"reads": [{"table": "artpieces", "columns": ["focus", "postal_code"]}], "writes": [{"table": "bi.device_log", "columns": ["focus", "postal_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT grant_start_date, count_date FROM defense_project_timelines\", engine)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"green_building_projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "defense_project_timelines", "columns": ["grant_start_date", "count_date"]}], "writes": [{"table": "green_building_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO extraction_methods (characteristic_id, visit_year) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "extraction_methods", "columns": ["characteristic_id", "visit_year"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO authenticationlogs SELECT * FROM legacy\ncur.execute(\"SELECT menuitem, undergraduate FROM accessibility_audits LIMIT 335\")\n", "labels": {"reads": [{"table": "accessibility_audits", "columns": ["menuitem", "undergraduate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.stg_campaigns SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO recycledmaterialsgarments SELECT invoice_number, clean_jerk, artifactname FROM gamedesigndata WHERE invoice_number > 304\")\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": ["invoice_number", "clean_jerk", "artifactname"]}], "writes": [{"table": "recycledmaterialsgarments", "columns": ["invoice_number", "clean_jerk", "artifactname"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"catalog_contents\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "catalog_contents", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO legal_precedents SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"astronaut_missions\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"donation\")\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": null}], "writes": [{"table": "donation", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO field_rainfall SELECT * FROM legacy\nspark.sql(\"INSERT INTO convictions SELECT co2_reduction, grantamount, financing_date FROM military_sales WHERE co2_reduction > 305\")\n", "labels": {"reads": [{"table": "military_sales", "columns": ["co2_reduction", "grantamount", "financing_date"]}], "writes": [{"table": "convictions", "columns": ["co2_reduction", "grantamount", "financing_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM water_consumption\"\n", "labels": {"reads": [{"table": "water_consumption", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO jupiter_spacecraft SELECT * FROM legacy\nspark.sql(\"INSERT INTO teacher_professional_development SELECT feedid, productivity FROM workercontactinfo WHERE feedid > 441\")\n", "labels": {"reads": [{"table": "workercontactinfo", "columns": ["feedid", "productivity"]}], "writes": [{"table": "teacher_professional_development", "columns": ["feedid", "productivity"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO communitycourts SELECT * FROM legacy\ncur.execute(\"SELECT representative_name, cell_mobile_number FROM dispensarysales LIMIT 304\")\n", "labels": {"reads": [{"table": "dispensarysales", "columns": ["representative_name", "cell_mobile_number"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO species_data SELECT num_courses, trip_distance FROM market_access WHERE num_courses > 390\")\n", "labels": {"reads": [{"table": "market_access", "columns": ["num_courses", "trip_distance"]}], "writes": [{"table": "species_data", "columns": ["num_courses", "trip_distance"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"habitat_preservation\").toPandas()\ndf[[\"grant_start_date\", \"built_year\"]].to_sql(\"co_ownership\", engine, index=False)\n", "labels": {"reads": [{"table": "habitat_preservation", "columns": null}], "writes": [{"table": "co_ownership", "columns": ["grant_start_date", "built_year"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_coupon_use_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ref_colors\")\n", "labels": {"reads": [{"table": "dwd.dwd_coupon_use_df", "columns": null}], "writes": [{"table": "ref_colors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stock_levels SELECT citizen_id, price, last_workout_date FROM size WHERE citizen_id > 347\"\n", "labels": {"reads": [{"table": "size", "columns": ["citizen_id", "price", "last_workout_date"]}], "writes": [{"table": "stock_levels", "columns": ["citizen_id", "price", "last_workout_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM green_buildings\"\n", "labels": {"reads": [{"table": "green_buildings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM art_collection\", conn)\ndf.to_sql(\"flu_shots\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "art_collection", "columns": null}], "writes": [{"table": "flu_shots", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"policy\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"movies\")\n", "labels": {"reads": [{"table": "policy", "columns": null}], "writes": [{"table": "movies", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd.dwd_device_log_delta\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"show\")\nsrc.write.insertInto(\"galleries\", overwrite=True)\n", "labels": {"reads": [{"table": "show", "columns": null}], "writes": [{"table": "galleries", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ref_budget_codes\").where(\"dt = current_date()\").writeTo(\"club\").append()\n", "labels": {"reads": [{"table": "ref_budget_codes", "columns": null}], "writes": [{"table": "club", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO employee_demographics SELECT * FROM legacy\nspark.sql(\"INSERT INTO shipments SELECT number_of_platforms, certification_id FROM mountain WHERE number_of_platforms > 35\")\n", "labels": {"reads": [{"table": "mountain", "columns": ["number_of_platforms", "certification_id"]}], "writes": [{"table": "shipments", "columns": ["number_of_platforms", "certification_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO water_sources SELECT a.don_name, b.review_id FROM genetics.crispr a JOIN education_programs b ON a.trade_name = b.trade_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "genetics.crispr", "columns": null}, {"table": "education_programs", "columns": null}], "writes": [{"table": "water_sources", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ytterbium_supply\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws.dws_risk_score_df\");\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": null}], "writes": [{"table": "dws.dws_risk_score_df", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"feed\")\nsrc.write.insertInto(\"bi.bi_inventory_di\", overwrite=True)\n", "labels": {"reads": [{"table": "feed", "columns": null}], "writes": [{"table": "bi.bi_inventory_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mental_health_scores (customer_code, section_title) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mental_health_scores", "columns": ["customer_code", "section_title"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO reverselogisticstransactions SELECT quantity_containers, crop_type, mappinglength, views FROM dwd.exposure_hourly WHERE quantity_containers > 285\")\n", "labels": {"reads": [{"table": "dwd.exposure_hourly", "columns": ["quantity_containers", "crop_type", "mappinglength", "views"]}], "writes": [{"table": "reverselogisticstransactions", "columns": ["quantity_containers", "crop_type", "mappinglength", "views"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO departments SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dorm (count, funding_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dorm", "columns": ["count", "funding_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM teacher_development_race\", conn)\ndf.to_sql(\"languages\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": null}], "writes": [{"table": "languages", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT treatment_type, date_stored FROM levees LIMIT 442\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO government.city SELECT cases_handled, machine_id, offset_id, billid FROM tryout WHERE cases_handled > 28\")\n", "labels": {"reads": [{"table": "levees", "columns": ["treatment_type", "date_stored"]}, {"table": "tryout", "columns": ["cases_handled", "machine_id", "offset_id", "billid"]}], "writes": [{"table": "government.city", "columns": ["cases_handled", "machine_id", "offset_id", "billid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vrusers SELECT sale_price, posted_at, emissions, equipment_id FROM astronaut_missions WHERE sale_price > 176\"\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": ["sale_price", "posted_at", "emissions", "equipment_id"]}], "writes": [{"table": "vrusers", "columns": ["sale_price", "posted_at", "emissions", "equipment_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT date_id, safety_id FROM engineer_visits LIMIT 306\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO visualartprograms SELECT opening_hours, fertilizer_id, program_date FROM news_stories WHERE opening_hours > 423\")\n", "labels": {"reads": [{"table": "engineer_visits", "columns": ["date_id", "safety_id"]}, {"table": "news_stories", "columns": ["opening_hours", "fertilizer_id", "program_date"]}], "writes": [{"table": "visualartprograms", "columns": ["opening_hours", "fertilizer_id", "program_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"council_tax\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"al_jazeera_data\")\n", "labels": {"reads": [{"table": "council_tax", "columns": null}], "writes": [{"table": "al_jazeera_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table strains --target-dir /tmp/land\n", "labels": {"reads": [{"table": "strains", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"trucks\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "trucks", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO band SELECT a.drug, b.destroyed_by_employee_id FROM appointment a JOIN bi.products_daily b ON a.census_ranking = b.census_ranking\"\n", "labels": {"reads": [{"table": "appointment", "columns": null}, {"table": "bi.products_daily", "columns": null}], "writes": [{"table": "band", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO eco_diversification_investment SELECT pet_age, well_id, governor, water_consumption FROM schools WHERE pet_age > 54\"\n", "labels": {"reads": [{"table": "schools", "columns": ["pet_age", "well_id", "governor", "water_consumption"]}], "writes": [{"table": "eco_diversification_investment", "columns": ["pet_age", "well_id", "governor", "water_consumption"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO gamesales SELECT a.arrival, b.amount_donated FROM dws.dws_events_hourly a JOIN reverselogisticstransactions b ON a.channel = b.channel\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dws.dws_events_hourly", "columns": null}, {"table": "reverselogisticstransactions", "columns": null}], "writes": [{"table": "gamesales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO sports SELECT institution, outcome, drill_count FROM studies WHERE institution > 169\");\n", "labels": {"reads": [{"table": "studies", "columns": ["institution", "outcome", "drill_count"]}], "writes": [{"table": "sports", "columns": ["institution", "outcome", "drill_count"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"flight_safety\")\nsrc.write.insertInto(\"platform\", overwrite=True)\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": [{"table": "platform", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"donationsbycause\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "donationsbycause", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"climatedata\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"province.human_rights_data\")\n", "labels": {"reads": [{"table": "climatedata", "columns": null}], "writes": [{"table": "province.human_rights_data", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO creativeais SELECT a.main_services, b.trainingdate FROM conservation a JOIN bi.bi_shipments b ON a.staff_name = b.staff_name\"\n", "labels": {"reads": [{"table": "conservation", "columns": null}, {"table": "bi.bi_shipments", "columns": null}], "writes": [{"table": "creativeais", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 222;\nSQL\n", "labels": {"reads": [{"table": "news_reporting", "columns": ["payment_type_code", "cargo_id"]}, {"table": "suburbs", "columns": ["num_of_component", "destination_id"]}], "writes": [{"table": "latam_schema.education_budget", "columns": ["num_of_component", "destination_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stores SELECT a.incorporated_in, b.song_name FROM community_programs a JOIN bi.bi_events_full b ON a.visitor_id = b.visitor_id\"\n", "labels": {"reads": [{"table": "community_programs", "columns": null}, {"table": "bi.bi_events_full", "columns": null}], "writes": [{"table": "stores", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO energy_prices SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"talent_acquisition\").where(\"dt = current_date()\").writeTo(\"bus_fare_collection\").append()\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": null}], "writes": [{"table": "bus_fare_collection", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi.bi_vendors_di\")\nsrc.write.insertInto(\"dws.cart_item_full\", overwrite=True)\n", "labels": {"reads": [{"table": "bi.bi_vendors_di", "columns": null}], "writes": [{"table": "dws.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"public_transportation_routes\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"renewable_projects\")\n", "labels": {"reads": [{"table": "public_transportation_routes", "columns": null}], "writes": [{"table": "renewable_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM open_pedagogy_exam\", conn)\ndf.to_sql(\"dws_coupon_use\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "open_pedagogy_exam", "columns": null}], "writes": [{"table": "dws_coupon_use", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_payments_delta SELECT * FROM legacy\ncur.execute(\"SELECT pilot, typical_buying_price FROM ods_exposure_delta LIMIT 136\")\n", "labels": {"reads": [{"table": "ods_exposure_delta", "columns": ["pilot", "typical_buying_price"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 67;\nSQL\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["goal_date", "num_beds"]}, {"table": "rural_feeder_roads", "columns": ["health_equity_metric_2", "trench_id", "advocate_name", "dock_count"]}], "writes": [{"table": "gene", "columns": ["health_equity_metric_2", "trench_id", "advocate_name", "dock_count"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"environmental_impact_stats\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.member_point\")\n", "labels": {"reads": [{"table": "environmental_impact_stats", "columns": null}], "writes": [{"table": "ads.member_point", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT fiscal_year, foreign FROM catalog_structure\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"wellbeing_program_participants\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "catalog_structure", "columns": ["fiscal_year", "foreign"]}], "writes": [{"table": "wellbeing_program_participants", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"erc20_transactions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO country_waste_generation SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"apartments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "apartments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT workername, years_operating FROM ship LIMIT 149\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ship", "columns": ["workername", "years_operating"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO ads.ads_cart_item_hourly SELECT a.billingcountry, b.attack_date FROM territory.human_rights_data a JOIN aircraft b ON a.tournament_name = b.tournament_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "territory.human_rights_data", "columns": null}, {"table": "aircraft", "columns": null}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO volunteerprograms SELECT year_built, export_country FROM fish_stock WHERE year_built > 288\")\n", "labels": {"reads": [{"table": "fish_stock", "columns": ["year_built", "export_country"]}], "writes": [{"table": "volunteerprograms", "columns": ["year_built", "export_country"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 114;\nEOF\n", "labels": {"reads": [{"table": "recruiters", "columns": ["number_of_vessels", "donor_state"]}], "writes": [{"table": "mart.mart_payments_hourly", "columns": ["number_of_vessels", "donor_state"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"startup_founders\")\nupsert_to_warehouse(df, \"cb_agreements\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "startup_founders", "columns": null}], "writes": [{"table": "cb_agreements", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ai_safety SELECT a.fare, b.species_id FROM dwd.products_hourly a JOIN whale_sightings b ON a.productionrate = b.productionrate\"\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": null}, {"table": "whale_sightings", "columns": null}], "writes": [{"table": "ai_safety", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO electricvehicleadoption SELECT a.supplier_id, b.policy_area FROM fleet_management a JOIN languagesatrisk b ON a.industry = b.industry\"\n", "labels": {"reads": [{"table": "fleet_management", "columns": null}, {"table": "languagesatrisk", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.median_home_value > 157).all()\n# src table: russia_nato_diplomacy\nengine.execute(\"INSERT INTO tourism_activities SELECT * FROM russia_nato_diplomacy\")\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": null}], "writes": [{"table": "tourism_activities", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO residents_services SELECT a.electoral_register_id, b.address_type_code FROM team_franchise a JOIN authors b ON a.committee = b.committee\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "team_franchise", "columns": null}, {"table": "authors", "columns": null}], "writes": [{"table": "residents_services", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mexico_regions SELECT item_type, order_shipping_charges, trip_duration FROM infantmortalitydata WHERE item_type > 490\"], check=True)\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": ["item_type", "order_shipping_charges", "trip_duration"]}], "writes": [{"table": "mexico_regions", "columns": ["item_type", "order_shipping_charges", "trip_duration"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"tokyo_motor_show\");\ndf.write().mode(\"overwrite\").saveAsTable(\"neighborhoods\");\n", "labels": {"reads": [{"table": "tokyo_motor_show", "columns": null}], "writes": [{"table": "neighborhoods", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"support\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "support", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"round\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"militarypersonnel\")\n", "labels": {"reads": [{"table": "round", "columns": null}], "writes": [{"table": "militarypersonnel", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ocean_acidity\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"cybersecuritybudget\")\n", "labels": {"reads": [{"table": "ocean_acidity", "columns": null}], "writes": [{"table": "cybersecuritybudget", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model roles depends on perpetrator\ndbt build --models roles --vars '{\"src\":\"perpetrator\"}'\n", "labels": {"reads": [{"table": "perpetrator", "columns": null}], "writes": [{"table": "roles", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table wellbeing_programs --columns stageposition,framework_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "wellbeing_programs", "columns": ["stageposition", "framework_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model restorative_justice_3 depends on vr_adopters\ndbt run --select restorative_justice_3 --vars '{\"source_table\":\"vr_adopters\"}'\n", "labels": {"reads": [{"table": "vr_adopters", "columns": null}], "writes": [{"table": "restorative_justice_3", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_coupon_use\").toPandas()\ndf[[\"low_income_neighborhood\", \"order_item_id\"]].to_sql(\"benefits_overpayments\", engine, index=False)\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": null}], "writes": [{"table": "benefits_overpayments", "columns": ["low_income_neighborhood", "order_item_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"product_details\")\nsrc.write.insertInto(\"songs\", overwrite=True)\n", "labels": {"reads": [{"table": "product_details", "columns": null}], "writes": [{"table": "songs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO livestock SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"atlantic_marine_life\");\ndf.write().mode(\"overwrite\").saveAsTable(\"store\");\n", "labels": {"reads": [{"table": "atlantic_marine_life", "columns": null}], "writes": [{"table": "store", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO stg.refunds SELECT seat_section, startyear FROM train WHERE seat_section > 323\");\n", "labels": {"reads": [{"table": "train", "columns": ["seat_section", "startyear"]}], "writes": [{"table": "stg.refunds", "columns": ["seat_section", "startyear"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT inclusive_housing_policy, forename FROM intelligence_personnel\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"check_ins\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": ["inclusive_housing_policy", "forename"]}], "writes": [{"table": "check_ins", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"circular_economy_initiatives\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "circular_economy_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"cosmetics\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"preferences\")\n", "labels": {"reads": [{"table": "cosmetics", "columns": null}], "writes": [{"table": "preferences", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"harvest_permits\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "harvest_permits", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM productivity\", conn)\ndf.to_sql(\"overwatch_scores\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "productivity", "columns": null}], "writes": [{"table": "overwatch_scores", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"vessel_tracking\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "vessel_tracking", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bus_fares SELECT a.hashtags, b.last_maintenance FROM ocean_shipping.cargo a JOIN waste_generation_metrics b ON a.post_category = b.post_category\"\n", "labels": {"reads": [{"table": "ocean_shipping.cargo", "columns": null}, {"table": "waste_generation_metrics", "columns": null}], "writes": [{"table": "bus_fares", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"agri_innov\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "agri_innov", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO editor SELECT * FROM legacy\ncur.execute(\"SELECT ai_adoption, department_id FROM infantmortalitydata LIMIT 103\")\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": ["ai_adoption", "department_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_agriculture_initiatives\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"malicious_activity\")\n", "labels": {"reads": [{"table": "urban_agriculture_initiatives", "columns": null}], "writes": [{"table": "malicious_activity", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO waste_management_projects SELECT a.decision, b.materialtype FROM mart.mart_refunds_hourly a JOIN fairtradecertifications b ON a.order_status_code = b.order_status_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mart.mart_refunds_hourly", "columns": null}, {"table": "fairtradecertifications", "columns": null}], "writes": [{"table": "waste_management_projects", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO acidification_data SELECT excavationid, practice FROM airport WHERE excavationid > 342\"\n", "labels": {"reads": [{"table": "airport", "columns": ["excavationid", "practice"]}], "writes": [{"table": "acidification_data", "columns": ["excavationid", "practice"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT case_burden, budgeted FROM teacher_professional_development\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"ads.ads_products_full\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "teacher_professional_development", "columns": ["case_burden", "budgeted"]}], "writes": [{"table": "ads.ads_products_full", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO carbon_offsets SELECT text_of_notes, commanding_officer, local_authority FROM num_employees WHERE text_of_notes > 439\")\n", "labels": {"reads": [{"table": "num_employees", "columns": ["text_of_notes", "commanding_officer", "local_authority"]}], "writes": [{"table": "carbon_offsets", "columns": ["text_of_notes", "commanding_officer", "local_authority"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"arrivals\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"urban_transportation\")\n", "labels": {"reads": [{"table": "arrivals", "columns": null}], "writes": [{"table": "urban_transportation", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart_vendors_full\", conn)\ndf.to_sql(\"institution\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart_vendors_full", "columns": null}], "writes": [{"table": "institution", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"retailerg\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"attorney_billing\")\n", "labels": {"reads": [{"table": "retailerg", "columns": null}], "writes": [{"table": "attorney_billing", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO courts (prominence, horizontal_bar_points) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "courts", "columns": ["prominence", "horizontal_bar_points"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO inclusive_housing SELECT stu_phone, games, violationtype FROM investments WHERE stu_phone > 69\"\n", "labels": {"reads": [{"table": "investments", "columns": ["stu_phone", "games", "violationtype"]}], "writes": [{"table": "inclusive_housing", "columns": ["stu_phone", "games", "violationtype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT plan_type, life_expectancy FROM operations\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"stg_orders_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "operations", "columns": ["plan_type", "life_expectancy"]}], "writes": [{"table": "stg_orders_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO lives_in SELECT a.exoplanet, b.home_games FROM trade_history a JOIN product_categories b ON a.prod_date = b.prod_date\"\n", "labels": {"reads": [{"table": "trade_history", "columns": null}, {"table": "product_categories", "columns": null}], "writes": [{"table": "lives_in", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"casebilling\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.risk_score_di\")\n", "labels": {"reads": [{"table": "casebilling", "columns": null}], "writes": [{"table": "stg.risk_score_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hometown, claim_header_id FROM staff_roles\", engine)\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"ai_for_social_good\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "staff_roles", "columns": ["hometown", "claim_header_id"]}], "writes": [{"table": "ai_for_social_good", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"parking_fines\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "parking_fines", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO sustainability_fact SELECT recycling_rate, capacity_percentage, attendance FROM instructors WHERE recycling_rate > 272\");\n", "labels": {"reads": [{"table": "instructors", "columns": ["recycling_rate", "capacity_percentage", "attendance"]}], "writes": [{"table": "sustainability_fact", "columns": ["recycling_rate", "capacity_percentage", "attendance"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM emissions\"\n", "labels": {"reads": [{"table": "emissions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO asia_events (min_salary, model) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "asia_events", "columns": ["min_salary", "model"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table investment_strategies --columns founding_year,case_status --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "investment_strategies", "columns": ["founding_year", "case_status"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO healthcare_budget SELECT a.offer_id, b.exhibition_id FROM stg.users a JOIN phone b ON a.machine_series = b.machine_series\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.users", "columns": null}, {"table": "phone", "columns": null}], "writes": [{"table": "healthcare_budget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO evsales SELECT * FROM legacy\nspark.sql(\"INSERT INTO heart_rate_data SELECT prof_num, hotel_chain_name FROM industrial_building_energy_efficiency WHERE prof_num > 181\")\n", "labels": {"reads": [{"table": "industrial_building_energy_efficiency", "columns": ["prof_num", "hotel_chain_name"]}], "writes": [{"table": "heart_rate_data", "columns": ["prof_num", "hotel_chain_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"research_vessels\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "research_vessels", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO invoice SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"pollutionincidents\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"defenseprojects\")\n", "labels": {"reads": [{"table": "pollutionincidents", "columns": null}], "writes": [{"table": "defenseprojects", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO inventory SELECT order_status, mission_category, head_id FROM cars WHERE order_status > 444\")\n", "labels": {"reads": [{"table": "cars", "columns": ["order_status", "mission_category", "head_id"]}], "writes": [{"table": "inventory", "columns": ["order_status", "mission_category", "head_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO ads.ads_campaigns_full SELECT target_name, reports_to, treasurer_vote, tour_type FROM stg.stg_device_log_daily WHERE target_name > 192\");\n", "labels": {"reads": [{"table": "stg.stg_device_log_daily", "columns": ["target_name", "reports_to", "treasurer_vote", "tour_type"]}], "writes": [{"table": "ads.ads_campaigns_full", "columns": ["target_name", "reports_to", "treasurer_vote", "tour_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO creative_ai_applications SELECT strain_name, join_year, is_accessible FROM audience_demographics WHERE strain_name > 223\")\n", "labels": {"reads": [{"table": "audience_demographics", "columns": ["strain_name", "join_year", "is_accessible"]}], "writes": [{"table": "creative_ai_applications", "columns": ["strain_name", "join_year", "is_accessible"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO labour_productivity SELECT a.meal_id, b.labor_practice FROM employeedata a JOIN military_personnel b ON a.field = b.field\"\n", "labels": {"reads": [{"table": "employeedata", "columns": null}, {"table": "military_personnel", "columns": null}], "writes": [{"table": "labour_productivity", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO oil_production SELECT a.hometeamid, b.restock_date FROM support_programs a JOIN european_healthcare b ON a.grape = b.grape\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "support_programs", "columns": null}, {"table": "european_healthcare", "columns": null}], "writes": [{"table": "oil_production", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 59;\nSQL\n", "labels": {"reads": [{"table": "train_station", "columns": ["site_id", "volunteer_quarter"]}, {"table": "evidence_based_policies", "columns": ["refugee_name", "concertid", "billing_state", "prepnurse"]}], "writes": [{"table": "sales_by_quarter", "columns": ["refugee_name", "concertid", "billing_state", "prepnurse"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"survey_data\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"marine_species_indian\")\n", "labels": {"reads": [{"table": "survey_data", "columns": null}], "writes": [{"table": "marine_species_indian", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fruitimport SELECT cancel_date, financially_capable, time_hour FROM ocean_basins WHERE cancel_date > 120\"\n", "labels": {"reads": [{"table": "ocean_basins", "columns": ["cancel_date", "financially_capable", "time_hour"]}], "writes": [{"table": "fruitimport", "columns": ["cancel_date", "financially_capable", "time_hour"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT news_outlet, part_name FROM third_party_companies\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"artcontributors\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "third_party_companies", "columns": ["news_outlet", "part_name"]}], "writes": [{"table": "artcontributors", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model initiatives depends on ads.refunds\ndbt build --models initiatives --vars '{\"source_table\":\"ads.refunds\"}'\n", "labels": {"reads": [{"table": "ads.refunds", "columns": null}], "writes": [{"table": "initiatives", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO life_expectancy SELECT a.trade_name, b.inventor_name FROM region_stats a JOIN well_production b ON a.co2_reduction = b.co2_reduction\"\n", "labels": {"reads": [{"table": "region_stats", "columns": null}, {"table": "well_production", "columns": null}], "writes": [{"table": "life_expectancy", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_orders_delta\"\n", "labels": {"reads": [{"table": "bi.bi_orders_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT tier, health_equity_metric_3 FROM public_participation\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"union_membership\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "public_participation", "columns": ["tier", "health_equity_metric_3"]}], "writes": [{"table": "union_membership", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stock\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "stock", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"employee_demographics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"railway\")\n", "labels": {"reads": [{"table": "employee_demographics", "columns": null}], "writes": [{"table": "railway", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"satisfaction\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws.dws_users_hourly\")\n", "labels": {"reads": [{"table": "satisfaction", "columns": null}], "writes": [{"table": "dws.dws_users_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT mgr_start_date, resource FROM iron\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"record\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "iron", "columns": ["mgr_start_date", "resource"]}], "writes": [{"table": "record", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO models_safety SELECT department_id, preferred_foot FROM supplier_addresses WHERE department_id > 393\")\n", "labels": {"reads": [{"table": "supplier_addresses", "columns": ["department_id", "preferred_foot"]}], "writes": [{"table": "models_safety", "columns": ["department_id", "preferred_foot"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO emergencyservices SELECT * FROM legacy\ncur.execute(\"SELECT apt_type_code, mine_type FROM ytterbium_supply LIMIT 266\")\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": ["apt_type_code", "mine_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"peacekeeping_units\")\nwrite_to_output(df, \"exit\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "peacekeeping_units", "columns": null}], "writes": [{"table": "exit", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.is_commercial > 430).all()\n# src table: ods.ods_coupon_use_delta\nengine.execute(\"INSERT INTO renewable_energy_investments SELECT * FROM ods.ods_coupon_use_delta\")\n", "labels": {"reads": [{"table": "ods.ods_coupon_use_delta", "columns": null}], "writes": [{"table": "renewable_energy_investments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_events_full\").toPandas()\ndf[[\"work_type\", \"destinationid\"]].to_sql(\"artcontributors\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}], "writes": [{"table": "artcontributors", "columns": ["work_type", "destinationid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tourism_activities SELECT date_of_ceremony, spent, productcategory FROM gardens WHERE date_of_ceremony > 245\"], check=True)\n", "labels": {"reads": [{"table": "gardens", "columns": ["date_of_ceremony", "spent", "productcategory"]}], "writes": [{"table": "tourism_activities", "columns": ["date_of_ceremony", "spent", "productcategory"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vehicle SELECT a.galleryname, b.owner FROM smart_contracts a JOIN defenseprojects b ON a.partner = b.partner\"\n", "labels": {"reads": [{"table": "smart_contracts", "columns": null}, {"table": "defenseprojects", "columns": null}], "writes": [{"table": "vehicle", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"financial_capability_id\");\ndf.write().mode(\"overwrite\").saveAsTable(\"participants_in_events\");\n", "labels": {"reads": [{"table": "financial_capability_id", "columns": null}], "writes": [{"table": "participants_in_events", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT bandmateid, name FROM transportation_fleet LIMIT 179\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO bi.inventory_delta SELECT permit_date, feedback_id, bike_id, transaction_id FROM dwd_coupon_use_hourly WHERE permit_date > 344\")\n", "labels": {"reads": [{"table": "transportation_fleet", "columns": ["bandmateid", "name"]}, {"table": "dwd_coupon_use_hourly", "columns": ["permit_date", "feedback_id", "bike_id", "transaction_id"]}], "writes": [{"table": "bi.inventory_delta", "columns": ["permit_date", "feedback_id", "bike_id", "transaction_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table station_company --target-dir /tmp/land\n", "labels": {"reads": [{"table": "station_company", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO building_permits SELECT * FROM legacy\ncur.execute(\"SELECT assigned_to_staff_id, product_color FROM excavations LIMIT 451\")\n", "labels": {"reads": [{"table": "excavations", "columns": ["assigned_to_staff_id", "product_color"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO performers SELECT cuisine_id, customer_email_address, order_quantity FROM emergencyservices WHERE cuisine_id > 177\"], check=True)\n", "labels": {"reads": [{"table": "emergencyservices", "columns": ["cuisine_id", "customer_email_address", "order_quantity"]}], "writes": [{"table": "performers", "columns": ["cuisine_id", "customer_email_address", "order_quantity"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO inst (center_name, scooter_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "inst", "columns": ["center_name", "scooter_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"assessment_notes\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "assessment_notes", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT treasurer_vote, strategy_id FROM mexico_regions LIMIT 37\")\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO communities SELECT ship_id, metric FROM disaster_response WHERE ship_id > 159\")\n", "labels": {"reads": [{"table": "mexico_regions", "columns": ["treasurer_vote", "strategy_id"]}, {"table": "disaster_response", "columns": ["ship_id", "metric"]}], "writes": [{"table": "communities", "columns": ["ship_id", "metric"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"ads_payments_hourly\")\nsrc.write.insertInto(\"gamegenres\", overwrite=True)\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": null}], "writes": [{"table": "gamegenres", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart_exposure_hourly SELECT site_id, emp_jobcode FROM ngo_funding WHERE site_id > 38\"], check=True)\n", "labels": {"reads": [{"table": "ngo_funding", "columns": ["site_id", "emp_jobcode"]}], "writes": [{"table": "mart_exposure_hourly", "columns": ["site_id", "emp_jobcode"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT digital, enddate FROM collective_bargaining LIMIT 59\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "collective_bargaining", "columns": ["digital", "enddate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model mart_refunds_di depends on reservations\ndbt run -s mart_refunds_di --vars '{\"source_table\":\"reservations\"}'\n", "labels": {"reads": [{"table": "reservations", "columns": null}], "writes": [{"table": "mart_refunds_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ai_safety_incidents SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bioprocess_engineering\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bioprocess_engineering", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO consumer SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT rows, outcome FROM music LIMIT 103\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ods.ods_sessions_df SELECT price_in_euros, year_built, category FROM basketball_teams WHERE price_in_euros > 123\")\n", "labels": {"reads": [{"table": "music", "columns": ["rows", "outcome"]}, {"table": "basketball_teams", "columns": ["price_in_euros", "year_built", "category"]}], "writes": [{"table": "ods.ods_sessions_df", "columns": ["price_in_euros", "year_built", "category"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ratings SELECT * FROM legacy\ncur.execute(\"SELECT trial_id, article_id FROM workforce_development_programs LIMIT 484\")\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": ["trial_id", "article_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"france_culture\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "france_culture", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO organization_contact_individuals SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO busmaintenance SELECT species_name, winery FROM ocean_acidification_antarctic WHERE species_name > 82\");\n", "labels": {"reads": [{"table": "ocean_acidification_antarctic", "columns": ["species_name", "winery"]}], "writes": [{"table": "busmaintenance", "columns": ["species_name", "winery"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"beauty_products\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads.ads_products_full\")\n", "labels": {"reads": [{"table": "beauty_products", "columns": null}], "writes": [{"table": "ads.ads_products_full", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"judges\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "judges", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO artist_demographics SELECT artifact_name, num_libraries, recycling_rate FROM shipmentinfo WHERE artifact_name > 403\"\n", "labels": {"reads": [{"table": "shipmentinfo", "columns": ["artifact_name", "num_libraries", "recycling_rate"]}], "writes": [{"table": "artist_demographics", "columns": ["artifact_name", "num_libraries", "recycling_rate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO atlantic_ocean SELECT a.attraction_id, b.bikes_available FROM laptimes a JOIN bikerental b ON a.crane_id = b.crane_id\"\n", "labels": {"reads": [{"table": "laptimes", "columns": null}, {"table": "bikerental", "columns": null}], "writes": [{"table": "atlantic_ocean", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.archeologist > 132).all()\n# src table: dwd.dwd_risk_score_delta\nengine.execute(\"INSERT INTO ucl_top10 SELECT * FROM dwd.dwd_risk_score_delta\")\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": null}], "writes": [{"table": "ucl_top10", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO asteroids (resource_name, organic) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "asteroids", "columns": ["resource_name", "organic"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mine\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"average\")\n", "labels": {"reads": [{"table": "mine", "columns": null}], "writes": [{"table": "average", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO mental_health_parity SELECT a.claim_id, b.sustainability_initiative_id FROM ads.ads_refunds_hourly a JOIN product b ON a.journalist_id = b.journalist_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.ads_refunds_hourly", "columns": null}, {"table": "product", "columns": null}], "writes": [{"table": "mental_health_parity", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO disease_prevalence SELECT diversity_score, session_language, date_of_attendance, wage FROM price_data WHERE diversity_score > 245\"\n", "labels": {"reads": [{"table": "price_data", "columns": ["diversity_score", "session_language", "date_of_attendance", "wage"]}], "writes": [{"table": "disease_prevalence", "columns": ["diversity_score", "session_language", "date_of_attendance", "wage"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT share_in_percent, outcome_type FROM biomes\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ndf.to_sql(\"test_drives\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "biomes", "columns": ["share_in_percent", "outcome_type"]}], "writes": [{"table": "test_drives", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO plots SELECT safetytestdate, oct, isfirstattendee, gamesplayed FROM platformstats WHERE safetytestdate > 451\");\n", "labels": {"reads": [{"table": "platformstats", "columns": ["safetytestdate", "oct", "isfirstattendee", "gamesplayed"]}], "writes": [{"table": "plots", "columns": ["safetytestdate", "oct", "isfirstattendee", "gamesplayed"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"field\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dw.users_hourly\")\n", "labels": {"reads": [{"table": "field", "columns": null}], "writes": [{"table": "dw.users_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO scan_dates SELECT last_name, document_structure_code FROM ads_payments_daily WHERE last_name > 286\"], check=True)\n", "labels": {"reads": [{"table": "ads_payments_daily", "columns": ["last_name", "document_structure_code"]}], "writes": [{"table": "scan_dates", "columns": ["last_name", "document_structure_code"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"mission\")\nsrc.write.insertInto(\"maintenancerequests\", overwrite=True)\n", "labels": {"reads": [{"table": "mission", "columns": null}], "writes": [{"table": "maintenancerequests", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 10;\nSQL\n", "labels": {"reads": [{"table": "classroom", "columns": ["product_description", "card_id"]}, {"table": "ads.users_full", "columns": ["workshop_name", "subscription_start_date"]}], "writes": [{"table": "laborstatistics", "columns": ["workshop_name", "subscription_start_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_source(ctx, \"platform_production\")\nexport_to_sink(df, \"participation\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "platform_production", "columns": null}], "writes": [{"table": "participation", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ods_payments_delta\");\ndf.write().mode(\"overwrite\").saveAsTable(\"sustainable_practices_2\");\n", "labels": {"reads": [{"table": "ods_payments_delta", "columns": null}], "writes": [{"table": "sustainable_practices_2", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table review --target-dir /tmp/land\n", "labels": {"reads": [{"table": "review", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"teams\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "teams", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sales_transaction_id, trend FROM dws.cart_item_full LIMIT 130\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": ["sales_transaction_id", "trend"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO bus_fares SELECT neighborhood, community_id FROM dws_coupon_use WHERE neighborhood > 26\"\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["neighborhood", "community_id"]}], "writes": [{"table": "bus_fares", "columns": ["neighborhood", "community_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO genetics.crispr SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO tvshows SELECT source_system_code, count_date, feature_details FROM investments WHERE source_system_code > 232\")\n", "labels": {"reads": [{"table": "investments", "columns": ["source_system_code", "count_date", "feature_details"]}], "writes": [{"table": "tvshows", "columns": ["source_system_code", "count_date", "feature_details"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO viewership SELECT centername, living_wage, hospital_id, funder FROM justice_schemas.legal_tech_providers WHERE centername > 153\");\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": ["centername", "living_wage", "hospital_id", "funder"]}], "writes": [{"table": "viewership", "columns": ["centername", "living_wage", "hospital_id", "funder"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM timber_sales\", conn)\ndf.to_sql(\"labor_stats\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "timber_sales", "columns": null}], "writes": [{"table": "labor_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO orgdonations SELECT fault_short_name, underrepresented_community FROM financial_capability_program WHERE fault_short_name > 92\"], check=True)\n", "labels": {"reads": [{"table": "financial_capability_program", "columns": ["fault_short_name", "underrepresented_community"]}], "writes": [{"table": "orgdonations", "columns": ["fault_short_name", "underrepresented_community"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dw_vendors_di\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dw_vendors_di", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"solar_energy\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "solar_energy", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.events (company_id, image_data) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.events", "columns": ["company_id", "image_data"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sustainable_urban_properties_2 (satellite_id, machine_series) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["satellite_id", "machine_series"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO renewable_projects SELECT exhibitioncountry, events FROM research.species WHERE exhibitioncountry > 208\");\n", "labels": {"reads": [{"table": "research.species", "columns": ["exhibitioncountry", "events"]}], "writes": [{"table": "renewable_projects", "columns": ["exhibitioncountry", "events"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"music_festival\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"academic_publications\")\n", "labels": {"reads": [{"table": "music_festival", "columns": null}], "writes": [{"table": "academic_publications", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.department > 403).all()\n# src table: user_reactions\nengine.execute(\"INSERT INTO drought_data SELECT * FROM user_reactions\")\n", "labels": {"reads": [{"table": "user_reactions", "columns": null}], "writes": [{"table": "drought_data", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO patients SELECT price, zone, sqft, request_id FROM latam_schema.education_budget WHERE price > 249\");\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": ["price", "zone", "sqft", "request_id"]}], "writes": [{"table": "patients", "columns": ["price", "zone", "sqft", "request_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"patient\").where(\"dt = current_date()\").writeTo(\"multimodal_trips\").append()\n", "labels": {"reads": [{"table": "patient", "columns": null}], "writes": [{"table": "multimodal_trips", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM arcticwildlifereserve\"\n", "labels": {"reads": [{"table": "arcticwildlifereserve", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 442;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_events_df", "columns": ["line_number", "school_code", "prof_num", "num_volunteers"]}], "writes": [{"table": "autoshow", "columns": ["line_number", "school_code", "prof_num", "num_volunteers"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO concert_sales (decor, funding) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "concert_sales", "columns": ["decor", "funding"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO branch SELECT a.coach_id, b.heartrate FROM station a JOIN dispensarysales b ON a.country_code = b.country_code\"\n", "labels": {"reads": [{"table": "station", "columns": null}, {"table": "dispensarysales", "columns": null}], "writes": [{"table": "branch", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table impact_investments --columns loan_amount,call_count --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "impact_investments", "columns": ["loan_amount", "call_count"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO textile_suppliers SELECT fare_amount, booked_count, international_passengers, avg_yield FROM vrusers WHERE fare_amount > 93\"\n", "labels": {"reads": [{"table": "vrusers", "columns": ["fare_amount", "booked_count", "international_passengers", "avg_yield"]}], "writes": [{"table": "textile_suppliers", "columns": ["fare_amount", "booked_count", "international_passengers", "avg_yield"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO bustrips SELECT quality, paper_id, subscribe_date FROM students_lifelong_learning WHERE quality > 119\");\n", "labels": {"reads": [{"table": "students_lifelong_learning", "columns": ["quality", "paper_id", "subscribe_date"]}], "writes": [{"table": "bustrips", "columns": ["quality", "paper_id", "subscribe_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"temperatureanomalies\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"open_data_initiatives\")\n", "labels": {"reads": [{"table": "temperatureanomalies", "columns": null}], "writes": [{"table": "open_data_initiatives", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO daily_revenue SELECT a.date_and_date, b.org FROM highest_scores a JOIN party_services b ON a.num_transactions = b.num_transactions\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "highest_scores", "columns": null}, {"table": "party_services", "columns": null}], "writes": [{"table": "daily_revenue", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.clicks_delta\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"menu_item\")\n", "labels": {"reads": [{"table": "ods.clicks_delta", "columns": null}], "writes": [{"table": "menu_item", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO forms SELECT student_id, users_engaged, campaign_id FROM emergency_calls WHERE student_id > 89\"], check=True)\n", "labels": {"reads": [{"table": "emergency_calls", "columns": ["student_id", "users_engaged", "campaign_id"]}], "writes": [{"table": "forms", "columns": ["student_id", "users_engaged", "campaign_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 306;\nSQL\n", "labels": {"reads": [{"table": "fireincidents", "columns": ["attendeename", "publication_id"]}, {"table": "gradeconversion", "columns": ["event_attendance", "education_id", "claim_type"]}], "writes": [{"table": "ads_refunds_full", "columns": ["event_attendance", "education_id", "claim_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT time_day, startyear FROM artist\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"upgrades\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "artist", "columns": ["time_day", "startyear"]}], "writes": [{"table": "upgrades", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO sustainableprojects SELECT 1\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO trainingprograms SELECT chemical_id, quantity_sold FROM manufacturermaterials WHERE chemical_id > 41\")\n", "labels": {"reads": [{"table": "manufacturermaterials", "columns": ["chemical_id", "quantity_sold"]}], "writes": [{"table": "trainingprograms", "columns": ["chemical_id", "quantity_sold"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 346;\nEOF\n", "labels": {"reads": [{"table": "financial_capability_id", "columns": ["screening_id", "property_price"]}], "writes": [{"table": "climate_finance_re", "columns": ["screening_id", "property_price"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO container_ships SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO communityengagements SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO pacific_ocean SELECT num_of_staff, size, cargo FROM ods.ods_member_point_delta WHERE num_of_staff > 205\");\n", "labels": {"reads": [{"table": "ods.ods_member_point_delta", "columns": ["num_of_staff", "size", "cargo"]}], "writes": [{"table": "pacific_ocean", "columns": ["num_of_staff", "size", "cargo"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dwd_risk_score_hourly SELECT program_type, orgid, account_number FROM device WHERE program_type > 468\"\n", "labels": {"reads": [{"table": "device", "columns": ["program_type", "orgid", "account_number"]}], "writes": [{"table": "dwd_risk_score_hourly", "columns": ["program_type", "orgid", "account_number"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO budget SELECT total_cost, account_name, num_libraries, aid_id FROM manufacturingplants WHERE total_cost > 117\")\n", "labels": {"reads": [{"table": "manufacturingplants", "columns": ["total_cost", "account_name", "num_libraries", "aid_id"]}], "writes": [{"table": "budget", "columns": ["total_cost", "account_name", "num_libraries", "aid_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO participates_in SELECT orgname, age_group_id, fleet_series FROM skincare_sales WHERE orgname > 310\"\n", "labels": {"reads": [{"table": "skincare_sales", "columns": ["orgname", "age_group_id", "fleet_series"]}], "writes": [{"table": "participates_in", "columns": ["orgname", "age_group_id", "fleet_series"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO satellites_in_orbit SELECT * FROM legacy\ncur.execute(\"SELECT end_station_name, access_date FROM vessel LIMIT 239\")\n", "labels": {"reads": [{"table": "vessel", "columns": ["end_station_name", "access_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"emergencyservices\").toPandas()\ndf[[\"state\", \"chromosome\"]].to_sql(\"time_dim\", engine, index=False)\n", "labels": {"reads": [{"table": "emergencyservices", "columns": null}], "writes": [{"table": "time_dim", "columns": ["state", "chromosome"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO donationsbycause SELECT a.host_country, b.living_wage FROM flu_shots a JOIN fare_segments b ON a.research_name = b.research_name\"\n", "labels": {"reads": [{"table": "flu_shots", "columns": null}, {"table": "fare_segments", "columns": null}], "writes": [{"table": "donationsbycause", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 161;\nEOF\n", "labels": {"reads": [{"table": "trainers", "columns": ["clean_date", "built"]}], "writes": [{"table": "asteroids", "columns": ["clean_date", "built"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO public.forest_stats SELECT a.menu_category, b.lastname FROM agency_satellites a JOIN performance_scores b ON a.financial_capability_score = b.financial_capability_score\"\n", "labels": {"reads": [{"table": "agency_satellites", "columns": null}, {"table": "performance_scores", "columns": null}], "writes": [{"table": "public.forest_stats", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO bus_fare_collection SELECT time_of_purchase, units_sold FROM virtual_tour_engagement WHERE time_of_purchase > 26\");\n", "labels": {"reads": [{"table": "virtual_tour_engagement", "columns": ["time_of_purchase", "units_sold"]}], "writes": [{"table": "bus_fare_collection", "columns": ["time_of_purchase", "units_sold"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"player_sessions\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"financial_capability_id\")\n", "labels": {"reads": [{"table": "player_sessions", "columns": null}], "writes": [{"table": "financial_capability_id", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"school_bus\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"routes\")\n", "labels": {"reads": [{"table": "school_bus", "columns": null}], "writes": [{"table": "routes", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM social_issues\", conn)\ndf.to_sql(\"dw.shipments_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "social_issues", "columns": null}], "writes": [{"table": "dw.shipments_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dorm_amenity\").toPandas()\ndf[[\"lesson_time\", \"donor_name\"]].to_sql(\"investor\", engine, index=False)\n", "labels": {"reads": [{"table": "dorm_amenity", "columns": null}], "writes": [{"table": "investor", "columns": ["lesson_time", "donor_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT room_number, attendees FROM professional_development LIMIT 164\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "professional_development", "columns": ["room_number", "attendees"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO government.city SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO captain (eco_friendly, closure_authorised_by_staff_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "captain", "columns": ["eco_friendly", "closure_authorised_by_staff_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO economic_diversification_projects SELECT a.total_distance, b.railway_id FROM tencel_sources a JOIN employeepromotions b ON a.well_depth = b.well_depth\"\n", "labels": {"reads": [{"table": "tencel_sources", "columns": null}, {"table": "employeepromotions", "columns": null}], "writes": [{"table": "economic_diversification_projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO supportprograms SELECT complaint_status_code, order_item_id, storm_id FROM stg.stg_exposure_daily WHERE complaint_status_code > 182\");\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": ["complaint_status_code", "order_item_id", "storm_id"]}], "writes": [{"table": "supportprograms", "columns": ["complaint_status_code", "order_item_id", "storm_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO animal_rehab SELECT home_team_score, productionid, jun, launch_company FROM sustainable_projects WHERE home_team_score > 402\"\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["home_team_score", "productionid", "jun", "launch_company"]}], "writes": [{"table": "animal_rehab", "columns": ["home_team_score", "productionid", "jun", "launch_company"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO threat_intelligence_budget SELECT union_member, donor_country FROM therapy_attendance WHERE union_member > 31\"\n", "labels": {"reads": [{"table": "therapy_attendance", "columns": ["union_member", "donor_country"]}], "writes": [{"table": "threat_intelligence_budget", "columns": ["union_member", "donor_country"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dwd.sessions\").where(\"dt = current_date()\").writeTo(\"waste_generation_metrics\").append()\n", "labels": {"reads": [{"table": "dwd.sessions", "columns": null}], "writes": [{"table": "waste_generation_metrics", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT workshop_group_id, product_type FROM galleryc LIMIT 449\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO goals SELECT retweets, plantlocation FROM farmers_india WHERE retweets > 198\")\n", "labels": {"reads": [{"table": "galleryc", "columns": ["workshop_group_id", "product_type"]}, {"table": "farmers_india", "columns": ["retweets", "plantlocation"]}], "writes": [{"table": "goals", "columns": ["retweets", "plantlocation"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bank_info\").where(\"dt = current_date()\").writeTo(\"stellar_transactions\").append()\n", "labels": {"reads": [{"table": "bank_info", "columns": null}], "writes": [{"table": "stellar_transactions", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"investments_esg\").where(\"dt = current_date()\").writeTo(\"elimination\").append()\n", "labels": {"reads": [{"table": "investments_esg", "columns": null}], "writes": [{"table": "elimination", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"feed\").where(\"dt = current_date()\").writeTo(\"architect\").append()\n", "labels": {"reads": [{"table": "feed", "columns": null}], "writes": [{"table": "architect", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO weapons SELECT enzyme_id, rating_in_percent, gross_worldwide FROM trucks WHERE enzyme_id > 190\"], check=True)\n", "labels": {"reads": [{"table": "trucks", "columns": ["enzyme_id", "rating_in_percent", "gross_worldwide"]}], "writes": [{"table": "weapons", "columns": ["enzyme_id", "rating_in_percent", "gross_worldwide"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dws.dws_events_hourly\").where(\"dt = current_date()\").writeTo(\"conditions\").append()\n", "labels": {"reads": [{"table": "dws.dws_events_hourly", "columns": null}], "writes": [{"table": "conditions", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO accelerator_compatible_browser (voter_id, actual_delivery_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "accelerator_compatible_browser", "columns": ["voter_id", "actual_delivery_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table factory_connections --columns violation_type,source_u_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "factory_connections", "columns": ["violation_type", "source_u_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stores_2 SELECT * FROM legacy\ncur.execute(\"SELECT subscribe_date, truck_details FROM scientists LIMIT 441\")\n", "labels": {"reads": [{"table": "scientists", "columns": ["subscribe_date", "truck_details"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO noise_pollution SELECT minename, statement_details FROM ref_incident_type WHERE minename > 37\")\n", "labels": {"reads": [{"table": "ref_incident_type", "columns": ["minename", "statement_details"]}], "writes": [{"table": "noise_pollution", "columns": ["minename", "statement_details"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO developers SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT founder_count, role_name FROM mappinglengths\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"mart.mart_payments_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mappinglengths", "columns": ["founder_count", "role_name"]}], "writes": [{"table": "mart.mart_payments_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 228;\nEOF\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles_japan", "columns": ["awardid", "deaths", "division"]}], "writes": [{"table": "algorithmic_fairness", "columns": ["awardid", "deaths", "division"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO renewable_energy_investments SELECT * FROM legacy\nspark.sql(\"INSERT INTO gamesessions SELECT retailer_id, statement_id FROM gamedesign WHERE retailer_id > 234\")\n", "labels": {"reads": [{"table": "gamedesign", "columns": ["retailer_id", "statement_id"]}], "writes": [{"table": "gamesessions", "columns": ["retailer_id", "statement_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ap_budget\", conn)\ndf.to_sql(\"coowners\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ap_budget", "columns": null}], "writes": [{"table": "coowners", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mobile_plans --columns experiment_name,platformname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mobile_plans", "columns": ["experiment_name", "platformname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 392;\nEOF\n", "labels": {"reads": [{"table": "gamedesign", "columns": ["member_id", "consultations"]}], "writes": [{"table": "financialwellbeing", "columns": ["member_id", "consultations"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.device_log\"\n", "labels": {"reads": [{"table": "bi.device_log", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.stg_users_di SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO veteran_unemployment SELECT a.initiative_region, b.work_type FROM nutritionfacts a JOIN transportation_per_country b ON a.diagnosis = b.diagnosis\"\n", "labels": {"reads": [{"table": "nutritionfacts", "columns": null}, {"table": "transportation_per_country", "columns": null}], "writes": [{"table": "veteran_unemployment", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO satisfaction SELECT request_id, supplychainid, premise_id FROM dwd.vendors WHERE request_id > 195\"], check=True)\n", "labels": {"reads": [{"table": "dwd.vendors", "columns": ["request_id", "supplychainid", "premise_id"]}], "writes": [{"table": "satisfaction", "columns": ["request_id", "supplychainid", "premise_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO satellite_missions_large (cows, improvement) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "satellite_missions_large", "columns": ["cows", "improvement"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"state_info\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"cultural_competency\")\n", "labels": {"reads": [{"table": "state_info", "columns": null}], "writes": [{"table": "cultural_competency", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO navalequipmentmaintenance (data_type, carbon_offset_tons) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "navalequipmentmaintenance", "columns": ["data_type", "carbon_offset_tons"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table digital_divide_initiatives --target-dir /tmp/land\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"defense_contracts\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"party_host\")\n", "labels": {"reads": [{"table": "defense_contracts", "columns": null}], "writes": [{"table": "party_host", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"inclusivehousing.affordablehousing\")\nsrc.write.insertInto(\"product_details\", overwrite=True)\n", "labels": {"reads": [{"table": "inclusivehousing.affordablehousing", "columns": null}], "writes": [{"table": "product_details", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"courts\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "courts", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 377;\nEOF\n", "labels": {"reads": [{"table": "artpieces", "columns": ["customer_address_id", "news_story_id", "kills", "nid"]}], "writes": [{"table": "textile_sourcing", "columns": ["customer_address_id", "news_story_id", "kills", "nid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.assists > 312).all()\n# src table: africa_projects\nengine.execute(\"INSERT INTO therapy_attendance SELECT * FROM africa_projects\")\n", "labels": {"reads": [{"table": "africa_projects", "columns": null}], "writes": [{"table": "therapy_attendance", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nsql = \"INSERT INTO ucl_top10 SELECT a.date_of_latest_revision, b.portname FROM sports a JOIN device_usage b ON a.menu_type = b.menu_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sports", "columns": null}, {"table": "device_usage", "columns": null}], "writes": [{"table": "ucl_top10", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws.events\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dws.events", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO sales_by_quarter (amount_paid, seat_section) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "sales_by_quarter", "columns": ["amount_paid", "seat_section"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"fair_wages\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "fair_wages", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO ai_safety_papers2 SELECT veteran_unemployment_rate, stockid, trial_year FROM stg.stg_risk_score_hourly WHERE veteran_unemployment_rate > 249\")\n", "labels": {"reads": [{"table": "stg.stg_risk_score_hourly", "columns": ["veteran_unemployment_rate", "stockid", "trial_year"]}], "writes": [{"table": "ai_safety_papers2", "columns": ["veteran_unemployment_rate", "stockid", "trial_year"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"gamedata\").where(\"dt = current_date()\").writeTo(\"city_budgets\").append()\n", "labels": {"reads": [{"table": "gamedata", "columns": null}], "writes": [{"table": "city_budgets", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table healthcareaccess --columns donation_date,gtype --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "healthcareaccess", "columns": ["donation_date", "gtype"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.vendors_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"agriculturalinvestments\")\n", "labels": {"reads": [{"table": "mart.vendors_full", "columns": null}], "writes": [{"table": "agriculturalinvestments", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM visualartprograms\", conn)\ndf.to_sql(\"artsales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "visualartprograms", "columns": null}], "writes": [{"table": "artsales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.leader_name > 335).all()\n# src table: undergoes\nengine.execute(\"INSERT INTO police_stations SELECT * FROM undergoes\")\n", "labels": {"reads": [{"table": "undergoes", "columns": null}], "writes": [{"table": "police_stations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO policy SELECT member_in_charge_id, nominee, max_temperature_f, deaths FROM fan_purchases WHERE member_in_charge_id > 151\")\n", "labels": {"reads": [{"table": "fan_purchases", "columns": ["member_in_charge_id", "nominee", "max_temperature_f", "deaths"]}], "writes": [{"table": "policy", "columns": ["member_in_charge_id", "nominee", "max_temperature_f", "deaths"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO temperatureanomalies SELECT sitename, description FROM stg.stg_exposure_daily WHERE sitename > 76\")\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": ["sitename", "description"]}], "writes": [{"table": "temperatureanomalies", "columns": ["sitename", "description"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 469;\nSQL\n", "labels": {"reads": [{"table": "historicalcontexts", "columns": ["order_date", "strain_id"]}, {"table": "gamereviews", "columns": ["treasurer_vote", "word_count", "num_hotels"]}], "writes": [{"table": "researchprojects", "columns": ["treasurer_vote", "word_count", "num_hotels"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model player_sessions depends on art_pieces\ndbt run --select player_sessions --vars 'source: art_pieces'\n", "labels": {"reads": [{"table": "art_pieces", "columns": null}], "writes": [{"table": "player_sessions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tech_accessibility_funding SELECT practice, coalquantity, conferencename FROM heritagesites WHERE practice > 228\"], check=True)\n", "labels": {"reads": [{"table": "heritagesites", "columns": ["practice", "coalquantity", "conferencename"]}], "writes": [{"table": "tech_accessibility_funding", "columns": ["practice", "coalquantity", "conferencename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"runs\").toPandas()\ndf[[\"state_code\", \"hireyear\"]].to_sql(\"extraction_methods\", engine, index=False)\n", "labels": {"reads": [{"table": "runs", "columns": null}], "writes": [{"table": "extraction_methods", "columns": ["state_code", "hireyear"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"student_tests_taken\");\ndf.write().mode(\"overwrite\").saveAsTable(\"incidents\");\n", "labels": {"reads": [{"table": "student_tests_taken", "columns": null}], "writes": [{"table": "incidents", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO urban_initiatives (posted_at, visit_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "urban_initiatives", "columns": ["posted_at", "visit_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO workforce_development_programs (location_description, donator_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "workforce_development_programs", "columns": ["location_description", "donator_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"shariah_financing\").toPandas()\ndf[[\"order_status_code\", \"effort\"]].to_sql(\"ods.ods_payments_full\", engine, index=False)\n", "labels": {"reads": [{"table": "shariah_financing", "columns": null}], "writes": [{"table": "ods.ods_payments_full", "columns": ["order_status_code", "effort"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.mart_users_di (guest_id, section_title) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_users_di", "columns": ["guest_id", "section_title"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO state_budget SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM facility\"\n", "labels": {"reads": [{"table": "facility", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads.ads_vendors_hourly\"\n", "labels": {"reads": [{"table": "ads.ads_vendors_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO inclusion_efforts SELECT wins, long FROM dws.coupon_use_di WHERE wins > 327\");\n", "labels": {"reads": [{"table": "dws.coupon_use_di", "columns": ["wins", "long"]}], "writes": [{"table": "inclusion_efforts", "columns": ["wins", "long"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM lessons\", conn)\ndf.to_sql(\"match\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "lessons", "columns": null}], "writes": [{"table": "match", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO traffic_violations SELECT a.astronaut_name, b.conferenceid FROM military_bases a JOIN team_revenue b ON a.start_therapy = b.start_therapy\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "military_bases", "columns": null}, {"table": "team_revenue", "columns": null}], "writes": [{"table": "traffic_violations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO spacecraft_manufacturing SELECT arrival_time, agreementid FROM iron WHERE arrival_time > 499\")\n", "labels": {"reads": [{"table": "iron", "columns": ["arrival_time", "agreementid"]}], "writes": [{"table": "spacecraft_manufacturing", "columns": ["arrival_time", "agreementid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO state_water_usage SELECT student_name, lender_name, violation_count, complaint_type_code FROM chargingstations WHERE student_name > 343\"], check=True)\n", "labels": {"reads": [{"table": "chargingstations", "columns": ["student_name", "lender_name", "violation_count", "complaint_type_code"]}], "writes": [{"table": "state_water_usage", "columns": ["student_name", "lender_name", "violation_count", "complaint_type_code"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd.products_hourly\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO scientists SELECT genre, mentalhealthscore, employee_name FROM participation WHERE genre > 411\"\n", "labels": {"reads": [{"table": "participation", "columns": ["genre", "mentalhealthscore", "employee_name"]}], "writes": [{"table": "scientists", "columns": ["genre", "mentalhealthscore", "employee_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table dysprosiumproduction --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.project_name > 140).all()\n# src table: water_treatment_facilities\nengine.execute(\"INSERT INTO animal_population_status SELECT * FROM water_treatment_facilities\")\n", "labels": {"reads": [{"table": "water_treatment_facilities", "columns": null}], "writes": [{"table": "animal_population_status", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO members SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO droughthistory SELECT language, party_email, event_attendance, pressure FROM bi.bi_payments_df WHERE language > 165\"], check=True)\n", "labels": {"reads": [{"table": "bi.bi_payments_df", "columns": ["language", "party_email", "event_attendance", "pressure"]}], "writes": [{"table": "droughthistory", "columns": ["language", "party_email", "event_attendance", "pressure"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table foodsafetyrecords --target-dir /tmp/land\n", "labels": {"reads": [{"table": "foodsafetyrecords", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO food_items SELECT a.royal_family_details, b.contact_number FROM water_distribution a JOIN carbon_pricing b ON a.investment_date = b.investment_date\"\n", "labels": {"reads": [{"table": "water_distribution", "columns": null}, {"table": "carbon_pricing", "columns": null}], "writes": [{"table": "food_items", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO news_report SELECT phase, reported FROM ai_papers WHERE phase > 48\")\n", "labels": {"reads": [{"table": "ai_papers", "columns": ["phase", "reported"]}], "writes": [{"table": "news_report", "columns": ["phase", "reported"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"wildlife_sanctuaries\").where(\"dt = current_date()\").writeTo(\"co2emissions\").append()\n", "labels": {"reads": [{"table": "wildlife_sanctuaries", "columns": null}], "writes": [{"table": "co2emissions", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT word_count, sid FROM incidents_by_month\", engine)\nimport logging\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"shop\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "incidents_by_month", "columns": ["word_count", "sid"]}], "writes": [{"table": "shop", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO bi.bi_risk_score_delta SELECT a.missiontype, b.studio FROM restaurants_tx a JOIN trafficviolations b ON a.word_count = b.word_count\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "restaurants_tx", "columns": null}, {"table": "trafficviolations", "columns": null}], "writes": [{"table": "bi.bi_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO union_membership SELECT farmer_id, lesson_time FROM commercialbuildings WHERE farmer_id > 256\"\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": ["farmer_id", "lesson_time"]}], "writes": [{"table": "union_membership", "columns": ["farmer_id", "lesson_time"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 61;\nSQL\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": ["personnelid", "price_range"]}, {"table": "person", "columns": ["major", "vehicle_model"]}], "writes": [{"table": "savings_programs", "columns": ["major", "vehicle_model"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 272;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_campaigns_delta", "columns": ["innovation", "start_year"]}], "writes": [{"table": "railway", "columns": ["innovation", "start_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 261;\nSQL\n", "labels": {"reads": [{"table": "school_bus", "columns": ["therapy_date", "violation_type"]}, {"table": "gymc_members", "columns": ["potency", "inspectiondate"]}], "writes": [{"table": "product_characteristics", "columns": ["potency", "inspectiondate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 431;\nSQL\n", "labels": {"reads": [{"table": "midwest_region", "columns": ["individual_first_name", "nation"]}, {"table": "marine_life_research_stations", "columns": ["area_id", "enrollment", "district", "draft_details"]}], "writes": [{"table": "sports", "columns": ["area_id", "enrollment", "district", "draft_details"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO geneva_motor_show SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_finance_organizations\"\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO healthcare SELECT fiscal_year, document_type_name FROM catalog_structure WHERE fiscal_year > 339\");\n", "labels": {"reads": [{"table": "catalog_structure", "columns": ["fiscal_year", "document_type_name"]}], "writes": [{"table": "healthcare", "columns": ["fiscal_year", "document_type_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO recyclingrates SELECT * FROM legacy\ncur.execute(\"SELECT dose, shop_name FROM warehouses LIMIT 75\")\n", "labels": {"reads": [{"table": "warehouses", "columns": ["dose", "shop_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"teams_mascots\")\nsink_to_sink(df, \"ods.clicks_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "teams_mascots", "columns": null}], "writes": [{"table": "ods.clicks_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.transportation_method > 252).all()\n# src table: stellar_transactions\nengine.execute(\"INSERT INTO customer_payments SELECT * FROM stellar_transactions\")\n", "labels": {"reads": [{"table": "stellar_transactions", "columns": null}], "writes": [{"table": "customer_payments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO military_bases SELECT games, max_aperture, total_attendance, centername FROM furniture WHERE games > 298\"\n", "labels": {"reads": [{"table": "furniture", "columns": ["games", "max_aperture", "total_attendance", "centername"]}], "writes": [{"table": "military_bases", "columns": ["games", "max_aperture", "total_attendance", "centername"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"vehicle_data\")\nupsert_to_sink(df, \"drug_approvals\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vehicle_data", "columns": null}], "writes": [{"table": "drug_approvals", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO cybersecurityincidents SELECT trip_start_time, bname, ticket_id FROM tech_workers_union WHERE trip_start_time > 41\")\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": ["trip_start_time", "bname", "ticket_id"]}], "writes": [{"table": "cybersecurityincidents", "columns": ["trip_start_time", "bname", "ticket_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO visitor_statistics SELECT impactid, fleet_id FROM london.stations WHERE impactid > 349\"], check=True)\n", "labels": {"reads": [{"table": "london.stations", "columns": ["impactid", "fleet_id"]}], "writes": [{"table": "visitor_statistics", "columns": ["impactid", "fleet_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO company_info SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT course_id, recycled FROM total_capacity\", engine)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ndf.to_sql(\"episodes\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "total_capacity", "columns": ["course_id", "recycled"]}], "writes": [{"table": "episodes", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart_shipments_full\"\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"securityincidents\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "securityincidents", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 339;\nSQL\n", "labels": {"reads": [{"table": "mentalhealthprofessional", "columns": ["winning_pilot", "restaurantname"]}, {"table": "has_allergy", "columns": ["flno", "gametype"]}], "writes": [{"table": "tourdifferences", "columns": ["flno", "gametype"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model subjects depends on pilot_record\ndbt run -s subjects --vars '{\"source_table\":\"pilot_record\"}'\n", "labels": {"reads": [{"table": "pilot_record", "columns": null}], "writes": [{"table": "subjects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO impact_asia SELECT a.stationid, b.installation_year FROM precipitation_data a JOIN mine b ON a.cancel_date = b.cancel_date\"\n", "labels": {"reads": [{"table": "precipitation_data", "columns": null}, {"table": "mine", "columns": null}], "writes": [{"table": "impact_asia", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"weather\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"weights\")\n", "labels": {"reads": [{"table": "weather", "columns": null}], "writes": [{"table": "weights", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT rate, safetytestdate FROM archaeologists\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"arcticwildlifereserve\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "archaeologists", "columns": ["rate", "safetytestdate"]}], "writes": [{"table": "arcticwildlifereserve", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO enrolled_in SELECT * FROM legacy\nspark.sql(\"INSERT INTO investment_rounds SELECT year, rural, airport_name, decor FROM grants WHERE year > 345\")\n", "labels": {"reads": [{"table": "grants", "columns": ["year", "rural", "airport_name", "decor"]}], "writes": [{"table": "investment_rounds", "columns": ["year", "rural", "airport_name", "decor"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM leed_buildings\", conn)\ndf.to_sql(\"organic_farms\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "leed_buildings", "columns": null}], "writes": [{"table": "organic_farms", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO event_attendance SELECT painting_name, funding_source_type, how_to_get_there, mediatorid FROM playergamehistory WHERE painting_name > 432\"], check=True)\n", "labels": {"reads": [{"table": "playergamehistory", "columns": ["painting_name", "funding_source_type", "how_to_get_there", "mediatorid"]}], "writes": [{"table": "event_attendance", "columns": ["painting_name", "funding_source_type", "how_to_get_there", "mediatorid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO grants SELECT cultural_significance, occupation, registered_date FROM mart.mart_products_df WHERE cultural_significance > 249\")\n", "labels": {"reads": [{"table": "mart.mart_products_df", "columns": ["cultural_significance", "occupation", "registered_date"]}], "writes": [{"table": "grants", "columns": ["cultural_significance", "occupation", "registered_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table regional_archaeologists --columns certification_name,menuitem --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "regional_archaeologists", "columns": ["certification_name", "menuitem"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO article_views SELECT testtypeid, streamid FROM mexico_regions WHERE testtypeid > 86\")\n", "labels": {"reads": [{"table": "mexico_regions", "columns": ["testtypeid", "streamid"]}], "writes": [{"table": "article_views", "columns": ["testtypeid", "streamid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 130;\nSQL\n", "labels": {"reads": [{"table": "recyclingrates", "columns": ["station", "veteran_id"]}, {"table": "artcontributors", "columns": ["org", "emergency_type"]}], "writes": [{"table": "artifact_analysis", "columns": ["org", "emergency_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"habitat_preservation\")\nsrc.write.insertInto(\"southeast_providers\", overwrite=True)\n", "labels": {"reads": [{"table": "habitat_preservation", "columns": null}], "writes": [{"table": "southeast_providers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"attendees\")\nsink_to_sink(df, \"green_building_materials\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "attendees", "columns": null}], "writes": [{"table": "green_building_materials", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"community_health_center\")\npersist_to_sink(df, \"manager_award\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "community_health_center", "columns": null}], "writes": [{"table": "manager_award", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"customer_policies\");\ndf.write().mode(\"overwrite\").saveAsTable(\"cybersecurity.strategies\");\n", "labels": {"reads": [{"table": "customer_policies", "columns": null}], "writes": [{"table": "cybersecurity.strategies", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO ref_product_categories SELECT tourist_attraction_id, factory_name, discount, item FROM ads.ads_risk_score_hourly WHERE tourist_attraction_id > 137\")\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": ["tourist_attraction_id", "factory_name", "discount", "item"]}], "writes": [{"table": "ref_product_categories", "columns": ["tourist_attraction_id", "factory_name", "discount", "item"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"staff\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bank_info\")\n", "labels": {"reads": [{"table": "staff", "columns": null}], "writes": [{"table": "bank_info", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO dw.dw_campaigns_di SELECT census_ranking, job_title, round_number, contract_number FROM construction_labor_stats WHERE census_ranking > 299\");\n", "labels": {"reads": [{"table": "construction_labor_stats", "columns": ["census_ranking", "job_title", "round_number", "contract_number"]}], "writes": [{"table": "dw.dw_campaigns_di", "columns": ["census_ranking", "job_title", "round_number", "contract_number"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO drug_approvals SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO lenders SELECT exhibitionname, risk_level, bioreactor_id, safety_record FROM stg_users_daily WHERE exhibitionname > 491\"], check=True)\n", "labels": {"reads": [{"table": "stg_users_daily", "columns": ["exhibitionname", "risk_level", "bioreactor_id", "safety_record"]}], "writes": [{"table": "lenders", "columns": ["exhibitionname", "risk_level", "bioreactor_id", "safety_record"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO inclusive_housing SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"menu_categories\")\nsave_to_target(df, \"activity\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "menu_categories", "columns": null}], "writes": [{"table": "activity", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO socialimpactinvestments SELECT individual_id, age_group FROM membership_data WHERE individual_id > 453\"\n", "labels": {"reads": [{"table": "membership_data", "columns": ["individual_id", "age_group"]}], "writes": [{"table": "socialimpactinvestments", "columns": ["individual_id", "age_group"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"public_transportation_routes\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"fruitimport\")\n", "labels": {"reads": [{"table": "public_transportation_routes", "columns": null}], "writes": [{"table": "fruitimport", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"agricultural_innovation\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dwd.campaigns\")\n", "labels": {"reads": [{"table": "agricultural_innovation", "columns": null}], "writes": [{"table": "dwd.campaigns", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO galleryc SELECT customer_id, loadingend, grantamount, number_of_vessels FROM safety_violations WHERE customer_id > 345\")\n", "labels": {"reads": [{"table": "safety_violations", "columns": ["customer_id", "loadingend", "grantamount", "number_of_vessels"]}], "writes": [{"table": "galleryc", "columns": ["customer_id", "loadingend", "grantamount", "number_of_vessels"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO editor SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO savings (product_price, cost) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "savings", "columns": ["product_price", "cost"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO aquatic_farms SELECT a.research_name, b.energy_consumption FROM mart.mart_member_point_df a JOIN bi.bi_campaigns_daily b ON a.is_recycled = b.is_recycled\"\n", "labels": {"reads": [{"table": "mart.mart_member_point_df", "columns": null}, {"table": "bi.bi_campaigns_daily", "columns": null}], "writes": [{"table": "aquatic_farms", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO savings_programs SELECT * FROM legacy\nspark.sql(\"INSERT INTO first_notification_of_loss SELECT courtid, time_month FROM farm WHERE courtid > 16\")\n", "labels": {"reads": [{"table": "farm", "columns": ["courtid", "time_month"]}], "writes": [{"table": "first_notification_of_loss", "columns": ["courtid", "time_month"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.users_full SELECT customer_number, reader_id FROM hockey_players WHERE customer_number > 163\"\n", "labels": {"reads": [{"table": "hockey_players", "columns": ["customer_number", "reader_id"]}], "writes": [{"table": "ads.users_full", "columns": ["customer_number", "reader_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO electric_buses SELECT province_name, heritage_site_id, insurancetype, area_id FROM attendees WHERE province_name > 462\"], check=True)\n", "labels": {"reads": [{"table": "attendees", "columns": ["province_name", "heritage_site_id", "insurancetype", "area_id"]}], "writes": [{"table": "electric_buses", "columns": ["province_name", "heritage_site_id", "insurancetype", "area_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO department_stores SELECT num_pallets, age_group_id, funding FROM functional_areas WHERE num_pallets > 95\"\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["num_pallets", "age_group_id", "funding"]}], "writes": [{"table": "department_stores", "columns": ["num_pallets", "age_group_id", "funding"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bike_station_info SELECT detection_date, price_in_dollars, mission_date, mental_health_rating FROM dwd.sessions WHERE detection_date > 219\"\n", "labels": {"reads": [{"table": "dwd.sessions", "columns": ["detection_date", "price_in_dollars", "mission_date", "mental_health_rating"]}], "writes": [{"table": "bike_station_info", "columns": ["detection_date", "price_in_dollars", "mission_date", "mental_health_rating"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO greenbuildings SELECT forest_type, amount_donated, has_disability FROM rooms WHERE forest_type > 392\")\n", "labels": {"reads": [{"table": "rooms", "columns": ["forest_type", "amount_donated", "has_disability"]}], "writes": [{"table": "greenbuildings", "columns": ["forest_type", "amount_donated", "has_disability"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO state_budget SELECT quality, state_province, vaccination_status FROM projecttimelinebybudget WHERE quality > 26\")\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": ["quality", "state_province", "vaccination_status"]}], "writes": [{"table": "state_budget", "columns": ["quality", "state_province", "vaccination_status"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"impact_asia\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "impact_asia", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"customer_month\");\ndf.write().mode(\"overwrite\").saveAsTable(\"gamegenres\");\n", "labels": {"reads": [{"table": "customer_month", "columns": null}], "writes": [{"table": "gamegenres", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO store_product (password, partner) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "store_product", "columns": ["password", "partner"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO rnd_budget SELECT savings, last_year FROM food_justice_orgs WHERE savings > 215\");\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": ["savings", "last_year"]}], "writes": [{"table": "rnd_budget", "columns": ["savings", "last_year"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"professional_development\");\ndf.write().mode(\"overwrite\").saveAsTable(\"vehicles\");\n", "labels": {"reads": [{"table": "professional_development", "columns": null}], "writes": [{"table": "vehicles", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"armed_forces\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "armed_forces", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO chemicalbatches SELECT investor_details, impact FROM biodiversity WHERE investor_details > 165\")\n", "labels": {"reads": [{"table": "biodiversity", "columns": ["investor_details", "impact"]}], "writes": [{"table": "chemicalbatches", "columns": ["investor_details", "impact"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"tech_for_social_good\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"problem_log\")\n", "labels": {"reads": [{"table": "tech_for_social_good", "columns": null}], "writes": [{"table": "problem_log", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table provinces --columns policy_number,waste_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "provinces", "columns": ["policy_number", "waste_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO incarcerated SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"customersregion\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.bi_shipments\")\n", "labels": {"reads": [{"table": "customersregion", "columns": null}], "writes": [{"table": "bi.bi_shipments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation_fleet\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "transportation_fleet", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"singer_in_concert\")\nsrc.write.insertInto(\"companies\", overwrite=True)\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": [{"table": "companies", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mentalhealthprofessional (excavationid, security_level) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mentalhealthprofessional", "columns": ["excavationid", "security_level"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO drug_approvals SELECT production_value, theatrename FROM company_info WHERE production_value > 291\"\n", "labels": {"reads": [{"table": "company_info", "columns": ["production_value", "theatrename"]}], "writes": [{"table": "drug_approvals", "columns": ["production_value", "theatrename"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dw.dw_users_di\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 318;\nSQL\n", "labels": {"reads": [{"table": "workshops", "columns": ["visit_month", "system_name"]}, {"table": "shariah_compliant_loans", "columns": ["workout_id", "sensor_reading", "coal_reserve_remaining"]}], "writes": [{"table": "stg.coupon_use", "columns": ["workout_id", "sensor_reading", "coal_reserve_remaining"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"development_hours\")\nsrc.write.insertInto(\"candidate_assessments\", overwrite=True)\n", "labels": {"reads": [{"table": "development_hours", "columns": null}], "writes": [{"table": "candidate_assessments", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO construction_labor SELECT media_literacy_score, participant, hours_contributed, recycling_rate FROM leed_buildings WHERE media_literacy_score > 35\")\n", "labels": {"reads": [{"table": "leed_buildings", "columns": ["media_literacy_score", "participant", "hours_contributed", "recycling_rate"]}], "writes": [{"table": "construction_labor", "columns": ["media_literacy_score", "participant", "hours_contributed", "recycling_rate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"agro_regions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "agro_regions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dws.dws_orders_full SELECT a.species_id, b.protein_name FROM roller_coaster a JOIN boston_emergency_response b ON a.founder_country = b.founder_country\"\n", "labels": {"reads": [{"table": "roller_coaster", "columns": null}, {"table": "boston_emergency_response", "columns": null}], "writes": [{"table": "dws.dws_orders_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 327;\nSQL\n", "labels": {"reads": [{"table": "waste_generation", "columns": ["facid", "monthly_rental"]}, {"table": "artists_valuation", "columns": ["latitude", "bats", "budget_in_billions"]}], "writes": [{"table": "intelligence_agency", "columns": ["latitude", "bats", "budget_in_billions"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO customer_size_diversity SELECT ocean, artworkname, home_team FROM on_call WHERE ocean > 219\");\n", "labels": {"reads": [{"table": "on_call", "columns": ["ocean", "artworkname", "home_team"]}], "writes": [{"table": "customer_size_diversity", "columns": ["ocean", "artworkname", "home_team"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO country_renewable_energy SELECT vulnerability_score, service_details, user_account, curator FROM ai_ethics_policies WHERE vulnerability_score > 155\")\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": ["vulnerability_score", "service_details", "user_account", "curator"]}], "writes": [{"table": "country_renewable_energy", "columns": ["vulnerability_score", "service_details", "user_account", "curator"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO ods_exposure_delta SELECT cuisine_id, state_name FROM agriculturalinvestments WHERE cuisine_id > 308\")\n", "labels": {"reads": [{"table": "agriculturalinvestments", "columns": ["cuisine_id", "state_name"]}], "writes": [{"table": "ods_exposure_delta", "columns": ["cuisine_id", "state_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO ods.ods_events_daily SELECT a.participant_details, b.address_details FROM culturalpractices a JOIN biosensor.patents b ON a.vehicle_flight_number = b.vehicle_flight_number\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "culturalpractices", "columns": null}, {"table": "biosensor.patents", "columns": null}], "writes": [{"table": "ods.ods_events_daily", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO geothermal_power_plants SELECT * FROM legacy\ncur.execute(\"SELECT head_id, neighborhood FROM donationprograms LIMIT 464\")\n", "labels": {"reads": [{"table": "donationprograms", "columns": ["head_id", "neighborhood"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sustainable_practices SELECT sale_date, environmental_impact FROM battery_projects WHERE sale_date > 170\"\n", "labels": {"reads": [{"table": "battery_projects", "columns": ["sale_date", "environmental_impact"]}], "writes": [{"table": "sustainable_practices", "columns": ["sale_date", "environmental_impact"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.bi_payments_full (practice, investmentid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_payments_full", "columns": ["practice", "investmentid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table influencers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "influencers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"equipmentsales\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"vendorfabrics\")\n", "labels": {"reads": [{"table": "equipmentsales", "columns": null}], "writes": [{"table": "vendorfabrics", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"maintenancerequests\");\ndf.write().mode(\"overwrite\").saveAsTable(\"rural.bus_trips\");\n", "labels": {"reads": [{"table": "maintenancerequests", "columns": null}], "writes": [{"table": "rural.bus_trips", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO shared_rides_tokyo SELECT a.famous_title, b.pricepergram FROM open_pedagogy_exam a JOIN membership b ON a.driver_id = b.driver_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "open_pedagogy_exam", "columns": null}, {"table": "membership", "columns": null}], "writes": [{"table": "shared_rides_tokyo", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO sustainable_fabrics SELECT ei_category, building_phone FROM autoshows WHERE ei_category > 86\"], check=True)\n", "labels": {"reads": [{"table": "autoshows", "columns": ["ei_category", "building_phone"]}], "writes": [{"table": "sustainable_fabrics", "columns": ["ei_category", "building_phone"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 162;\nEOF\n", "labels": {"reads": [{"table": "school_roster", "columns": ["professional_development", "hospital_name"]}], "writes": [{"table": "diversion_programs", "columns": ["professional_development", "hospital_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO constructors SELECT judge_state, productname, participation_date, sighting_date FROM festivals WHERE judge_state > 88\");\n", "labels": {"reads": [{"table": "festivals", "columns": ["judge_state", "productname", "participation_date", "sighting_date"]}], "writes": [{"table": "constructors", "columns": ["judge_state", "productname", "participation_date", "sighting_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"concentrateprices\").where(\"dt = current_date()\").writeTo(\"seafoodsouthafricakenya\").append()\n", "labels": {"reads": [{"table": "concentrateprices", "columns": null}], "writes": [{"table": "seafoodsouthafricakenya", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model atlantic_ocean_fish depends on unionnegotiations\ndbt run --models atlantic_ocean_fish --vars '{\"source_table\":\"unionnegotiations\"}'\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": null}], "writes": [{"table": "atlantic_ocean_fish", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cultural_heritage\").toPandas()\ndf[[\"review_rating\", \"business_zone\"]].to_sql(\"stg.stg_risk_score_df\", engine, index=False)\n", "labels": {"reads": [{"table": "cultural_heritage", "columns": null}], "writes": [{"table": "stg.stg_risk_score_df", "columns": ["review_rating", "business_zone"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO cybersecurity_strategies SELECT fouls, program_name, precip FROM coal WHERE fouls > 434\");\n", "labels": {"reads": [{"table": "coal", "columns": ["fouls", "program_name", "precip"]}], "writes": [{"table": "cybersecurity_strategies", "columns": ["fouls", "program_name", "precip"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 323;\nEOF\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": ["fda_approved", "brand_id"]}], "writes": [{"table": "street_markets", "columns": ["fda_approved", "brand_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO public_transport.passenger_count SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"band\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"donationhistory\")\n", "labels": {"reads": [{"table": "band", "columns": null}], "writes": [{"table": "donationhistory", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM plays_games\"\n", "labels": {"reads": [{"table": "plays_games", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO ocean_depths SELECT menu_category, friend, project_education, attack_country FROM school WHERE menu_category > 45\")\n", "labels": {"reads": [{"table": "school", "columns": ["menu_category", "friend", "project_education", "attack_country"]}], "writes": [{"table": "ocean_depths", "columns": ["menu_category", "friend", "project_education", "attack_country"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"countryintelligenceops\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "countryintelligenceops", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 218;\nEOF\n", "labels": {"reads": [{"table": "innovation_trends", "columns": ["maxoccupancy", "cases", "handling_date", "shelter_name"]}], "writes": [{"table": "southeast_providers", "columns": ["maxoccupancy", "cases", "handling_date", "shelter_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO brandrevenue SELECT mean_sea_level_pressure_inches, production_budget, credit_score FROM staff_members WHERE mean_sea_level_pressure_inches > 347\"], check=True)\n", "labels": {"reads": [{"table": "staff_members", "columns": ["mean_sea_level_pressure_inches", "production_budget", "credit_score"]}], "writes": [{"table": "brandrevenue", "columns": ["mean_sea_level_pressure_inches", "production_budget", "credit_score"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"agencies\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws.cart_item_full\");\n", "labels": {"reads": [{"table": "agencies", "columns": null}], "writes": [{"table": "dws.cart_item_full", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO soccer_teams SELECT u_id, police_force FROM bus_fare_collection WHERE u_id > 224\"\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": ["u_id", "police_force"]}], "writes": [{"table": "soccer_teams", "columns": ["u_id", "police_force"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM jupiter_spacecraft\"\n", "labels": {"reads": [{"table": "jupiter_spacecraft", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO satisfaction SELECT a.industry_4_0, b.violation_count FROM tech_for_social_good a JOIN fabrics b ON a.archaeologist_name = b.archaeologist_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tech_for_social_good", "columns": null}, {"table": "fabrics", "columns": null}], "writes": [{"table": "satisfaction", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.exposure_hourly\", conn)\ndf.to_sql(\"african_tourism\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.exposure_hourly", "columns": null}], "writes": [{"table": "african_tourism", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table military_personnel --target-dir /tmp/land\n", "labels": {"reads": [{"table": "military_personnel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.stageposition > 45).all()\n# src table: drug_sales\nengine.execute(\"INSERT INTO higher_ed.students SELECT * FROM drug_sales\")\n", "labels": {"reads": [{"table": "drug_sales", "columns": null}], "writes": [{"table": "higher_ed.students", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"animals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "animals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 121;\nEOF\n", "labels": {"reads": [{"table": "temperaturehistory", "columns": ["appointment_time", "school_id", "program_name", "open_date"]}], "writes": [{"table": "party_services", "columns": ["appointment_time", "school_id", "program_name", "open_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO dws_coupon_use SELECT vulnerability, composer FROM exhibition_visits WHERE vulnerability > 136\")\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": ["vulnerability", "composer"]}], "writes": [{"table": "dws_coupon_use", "columns": ["vulnerability", "composer"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM militaryoperations\", conn)\ndf.to_sql(\"coral_reefs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "militaryoperations", "columns": null}], "writes": [{"table": "coral_reefs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.stg_users_full SELECT no_of_customers, investor_id, statement_details, organizationid FROM virtual_tourism WHERE no_of_customers > 411\"\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": ["no_of_customers", "investor_id", "statement_details", "organizationid"]}], "writes": [{"table": "stg.stg_users_full", "columns": ["no_of_customers", "investor_id", "statement_details", "organizationid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO festivals SELECT orgid, club_name FROM police_stations WHERE orgid > 251\"\n", "labels": {"reads": [{"table": "police_stations", "columns": ["orgid", "club_name"]}], "writes": [{"table": "festivals", "columns": ["orgid", "club_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table rural_areas --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rural_areas", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT operation_id, part_id FROM safety_violations LIMIT 298\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "safety_violations", "columns": ["operation_id", "part_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO culturalcompetencytraining SELECT region_name, date_valid_to, equipment_type FROM journal WHERE region_name > 438\"\n", "labels": {"reads": [{"table": "journal", "columns": ["region_name", "date_valid_to", "equipment_type"]}], "writes": [{"table": "culturalcompetencytraining", "columns": ["region_name", "date_valid_to", "equipment_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ref_service_types SELECT * FROM legacy\ncur.execute(\"SELECT affiliation, professional_development_programs FROM operations LIMIT 325\")\n", "labels": {"reads": [{"table": "operations", "columns": ["affiliation", "professional_development_programs"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"operations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.dwd_vendors\")\n", "labels": {"reads": [{"table": "operations", "columns": null}], "writes": [{"table": "dwd.dwd_vendors", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"co_ownership_program\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"chemicalproducts\")\n", "labels": {"reads": [{"table": "co_ownership_program", "columns": null}], "writes": [{"table": "chemicalproducts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"fossil_fuel_vehicles\")\nsrc.write.insertInto(\"healthbudget\", overwrite=True)\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": null}], "writes": [{"table": "healthbudget", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.disaster_id > 104).all()\n# src table: rent_arrears\nengine.execute(\"INSERT INTO band SELECT * FROM rent_arrears\")\n", "labels": {"reads": [{"table": "rent_arrears", "columns": null}], "writes": [{"table": "band", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO esa_missions SELECT temporary_acting, ingredient_name FROM expenses WHERE temporary_acting > 265\"\n", "labels": {"reads": [{"table": "expenses", "columns": ["temporary_acting", "ingredient_name"]}], "writes": [{"table": "esa_missions", "columns": ["temporary_acting", "ingredient_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"paper_data\").where(\"dt = current_date()\").writeTo(\"stock\").append()\n", "labels": {"reads": [{"table": "paper_data", "columns": null}], "writes": [{"table": "stock", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ticket_sales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"temperature\")\n", "labels": {"reads": [{"table": "ticket_sales", "columns": null}], "writes": [{"table": "temperature", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM artcollection\", conn)\ndf.to_sql(\"document_locations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "artcollection", "columns": null}], "writes": [{"table": "document_locations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO wind_farms (parent_organization_id, fault_status) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "wind_farms", "columns": ["parent_organization_id", "fault_status"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO art_collection SELECT material_date, policy_holder_id, material_id, college_id FROM incident_region WHERE material_date > 245\");\n", "labels": {"reads": [{"table": "incident_region", "columns": ["material_date", "policy_holder_id", "material_id", "college_id"]}], "writes": [{"table": "art_collection", "columns": ["material_date", "policy_holder_id", "material_id", "college_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.is_accessible > 441).all()\n# src table: port_office\nengine.execute(\"INSERT INTO total_capacity SELECT * FROM port_office\")\n", "labels": {"reads": [{"table": "port_office", "columns": null}], "writes": [{"table": "total_capacity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table organic_products --columns tourists,mhw_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "organic_products", "columns": ["tourists", "mhw_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM debris\", conn)\ndf.to_sql(\"excavation_sites\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "debris", "columns": null}], "writes": [{"table": "excavation_sites", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO port_visits SELECT testdate, game_name, diversity_score FROM bike_share WHERE testdate > 371\")\n", "labels": {"reads": [{"table": "bike_share", "columns": ["testdate", "game_name", "diversity_score"]}], "writes": [{"table": "port_visits", "columns": ["testdate", "game_name", "diversity_score"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"digital_trends\")\nsave_to_output(df, \"indigenouscommunities\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "digital_trends", "columns": null}], "writes": [{"table": "indigenouscommunities", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO trips SELECT * FROM legacy\nspark.sql(\"INSERT INTO festivals SELECT shares, suppliername FROM ads_coupon_use_full WHERE shares > 485\")\n", "labels": {"reads": [{"table": "ads_coupon_use_full", "columns": ["shares", "suppliername"]}], "writes": [{"table": "festivals", "columns": ["shares", "suppliername"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO parties SELECT * FROM legacy\nspark.sql(\"INSERT INTO harvest_permits SELECT date_of_notes, fare_date FROM port_visits WHERE date_of_notes > 423\")\n", "labels": {"reads": [{"table": "port_visits", "columns": ["date_of_notes", "fare_date"]}], "writes": [{"table": "harvest_permits", "columns": ["date_of_notes", "fare_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 287;\nEOF\n", "labels": {"reads": [{"table": "aquaticfarm", "columns": ["booking_status_code", "engagementid"]}], "writes": [{"table": "electricvehicleadoption", "columns": ["booking_status_code", "engagementid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewableenergyprojects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"obesity\")\n", "labels": {"reads": [{"table": "renewableenergyprojects", "columns": null}], "writes": [{"table": "obesity", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO disease_prevalence SELECT port_code, floor_area_m2, completion_status FROM artpieces WHERE port_code > 246\"\n", "labels": {"reads": [{"table": "artpieces", "columns": ["port_code", "floor_area_m2", "completion_status"]}], "writes": [{"table": "disease_prevalence", "columns": ["port_code", "floor_area_m2", "completion_status"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO influencers SELECT destination_name, uk_vat_number, trip_duration FROM runs WHERE destination_name > 205\")\n", "labels": {"reads": [{"table": "runs", "columns": ["destination_name", "uk_vat_number", "trip_duration"]}], "writes": [{"table": "influencers", "columns": ["destination_name", "uk_vat_number", "trip_duration"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO vaccinations SELECT co_id, supplier_id, maintenanceid FROM funding_rounds WHERE co_id > 286\");\n", "labels": {"reads": [{"table": "funding_rounds", "columns": ["co_id", "supplier_id", "maintenanceid"]}], "writes": [{"table": "vaccinations", "columns": ["co_id", "supplier_id", "maintenanceid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table vaccine_administered --columns artifact_id,rank_in_round --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "vaccine_administered", "columns": ["artifact_id", "rank_in_round"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"maintenance_engineers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "maintenance_engineers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO waste_types SELECT prominence, ai_adoption, race_ethnicity_id FROM green_certification WHERE prominence > 11\");\n", "labels": {"reads": [{"table": "green_certification", "columns": ["prominence", "ai_adoption", "race_ethnicity_id"]}], "writes": [{"table": "waste_types", "columns": ["prominence", "ai_adoption", "race_ethnicity_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"az_drought_impact\").where(\"dt = current_date()\").writeTo(\"researcher\").append()\n", "labels": {"reads": [{"table": "az_drought_impact", "columns": null}], "writes": [{"table": "researcher", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"economic_diversification_efforts\");\ndf.write().mode(\"overwrite\").saveAsTable(\"habitat\");\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": null}], "writes": [{"table": "habitat", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table seal_population --columns song_id,dphone --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "seal_population", "columns": ["song_id", "dphone"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO extraction_methods SELECT a.initiative_region, b.member_name FROM geological_survey a JOIN carbon_pricing b ON a.artistname = b.artistname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "geological_survey", "columns": null}, {"table": "carbon_pricing", "columns": null}], "writes": [{"table": "extraction_methods", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO artsheritage SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO stats SELECT * FROM legacy\nspark.sql(\"INSERT INTO carbon_offset_initiatives SELECT condition_id, response_time, mean_humidity FROM issues WHERE condition_id > 467\")\n", "labels": {"reads": [{"table": "issues", "columns": ["condition_id", "response_time", "mean_humidity"]}], "writes": [{"table": "carbon_offset_initiatives", "columns": ["condition_id", "response_time", "mean_humidity"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO warehouses SELECT * FROM legacy\nspark.sql(\"INSERT INTO course_authors_and_tutors SELECT purchase_details, address, count_time, client_first_name FROM assets WHERE purchase_details > 85\")\n", "labels": {"reads": [{"table": "assets", "columns": ["purchase_details", "address", "count_time", "client_first_name"]}], "writes": [{"table": "course_authors_and_tutors", "columns": ["purchase_details", "address", "count_time", "client_first_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"furniture\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "furniture", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.customer_email_address > 471).all()\n# src table: marine_species_status\nengine.execute(\"INSERT INTO mentalhealthprofessional SELECT * FROM marine_species_status\")\n", "labels": {"reads": [{"table": "marine_species_status", "columns": null}], "writes": [{"table": "mentalhealthprofessional", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO bi_products SELECT * FROM legacy\nspark.sql(\"INSERT INTO team_revenue SELECT scoreid, num_employees, ingredient_name FROM medicine_enzyme_interaction WHERE scoreid > 230\")\n", "labels": {"reads": [{"table": "medicine_enzyme_interaction", "columns": ["scoreid", "num_employees", "ingredient_name"]}], "writes": [{"table": "team_revenue", "columns": ["scoreid", "num_employees", "ingredient_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"machine\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"state_budget\")\n", "labels": {"reads": [{"table": "machine", "columns": null}], "writes": [{"table": "state_budget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"features\").where(\"dt = current_date()\").writeTo(\"catalogs\").append()\n", "labels": {"reads": [{"table": "features", "columns": null}], "writes": [{"table": "catalogs", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"movie\").where(\"dt = current_date()\").writeTo(\"timber_production\").append()\n", "labels": {"reads": [{"table": "movie", "columns": null}], "writes": [{"table": "timber_production", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO dwd.events_daily SELECT num_students, subject_id FROM takes WHERE num_students > 301\")\n", "labels": {"reads": [{"table": "takes", "columns": ["num_students", "subject_id"]}], "writes": [{"table": "dwd.events_daily", "columns": ["num_students", "subject_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO uniteddefense.equipmentsales (case_id, trial_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "uniteddefense.equipmentsales", "columns": ["case_id", "trial_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM police_stations\"\n", "labels": {"reads": [{"table": "police_stations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO auto_show SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"coralreefs\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"equipmentsales\")\n", "labels": {"reads": [{"table": "coralreefs", "columns": null}], "writes": [{"table": "equipmentsales", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM provider_training\"\n", "labels": {"reads": [{"table": "provider_training", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM exhibition_record\"\n", "labels": {"reads": [{"table": "exhibition_record", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dwd.dwd_exposure_full SELECT * FROM legacy\nspark.sql(\"INSERT INTO crime_reports SELECT launch_company, born_state, start_time, menu_type FROM screen_mode WHERE launch_company > 371\")\n", "labels": {"reads": [{"table": "screen_mode", "columns": ["launch_company", "born_state", "start_time", "menu_type"]}], "writes": [{"table": "crime_reports", "columns": ["launch_company", "born_state", "start_time", "menu_type"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"asset_parts\")\nsrc.write.insertInto(\"overwatch_scores\", overwrite=True)\n", "labels": {"reads": [{"table": "asset_parts", "columns": null}], "writes": [{"table": "overwatch_scores", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"safetyincidents\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "safetyincidents", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ai_systems\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dwd.dwd_member_point_full\");\n", "labels": {"reads": [{"table": "ai_systems", "columns": null}], "writes": [{"table": "dwd.dwd_member_point_full", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model advocacy depends on trainmaintenance\ndbt build --models advocacy --vars '{\"src\":\"trainmaintenance\"}'\n", "labels": {"reads": [{"table": "trainmaintenance", "columns": null}], "writes": [{"table": "advocacy", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"art_collection\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "art_collection", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT award_date, problem_id FROM person LIMIT 330\")\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO museums SELECT outcome_date, recipient_id FROM dwd.dwd_campaigns WHERE outcome_date > 134\")\n", "labels": {"reads": [{"table": "person", "columns": ["award_date", "problem_id"]}, {"table": "dwd.dwd_campaigns", "columns": ["outcome_date", "recipient_id"]}], "writes": [{"table": "museums", "columns": ["outcome_date", "recipient_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO payments SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 94;\nSQL\n", "labels": {"reads": [{"table": "nyc_subway", "columns": ["moisture", "form_id"]}, {"table": "skills", "columns": ["project_type", "line_1_number_building"]}], "writes": [{"table": "bi.bi_events_full", "columns": ["project_type", "line_1_number_building"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO drug_approval SELECT * FROM legacy\ncur.execute(\"SELECT founder_identifies_as_lgbtq, garmentid FROM interventions LIMIT 110\")\n", "labels": {"reads": [{"table": "interventions", "columns": ["founder_identifies_as_lgbtq", "garmentid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.member_point_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"production_data\")\n", "labels": {"reads": [{"table": "stg.member_point_df", "columns": null}], "writes": [{"table": "production_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO county_public_safety SELECT affected_population, coupon_amount, scooter_id FROM organicproducts WHERE affected_population > 289\"\n", "labels": {"reads": [{"table": "organicproducts", "columns": ["affected_population", "coupon_amount", "scooter_id"]}], "writes": [{"table": "county_public_safety", "columns": ["affected_population", "coupon_amount", "scooter_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO therapy_sessions SELECT co_id, founder_race FROM dws.device_log_df WHERE co_id > 194\"\n", "labels": {"reads": [{"table": "dws.device_log_df", "columns": ["co_id", "founder_race"]}], "writes": [{"table": "therapy_sessions", "columns": ["co_id", "founder_race"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ods_vendors_daily SELECT common_name, city_code, item_type FROM product_characteristics WHERE common_name > 356\"\n", "labels": {"reads": [{"table": "product_characteristics", "columns": ["common_name", "city_code", "item_type"]}], "writes": [{"table": "ods_vendors_daily", "columns": ["common_name", "city_code", "item_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO match SELECT sessionid, contributorid, location_description FROM tb_reports WHERE sessionid > 427\"], check=True)\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["sessionid", "contributorid", "location_description"]}], "writes": [{"table": "match", "columns": ["sessionid", "contributorid", "location_description"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO daily_oil_production SELECT warehouse_id, maintenance_contract_id, hours_billed, therapeutic_area FROM bi.bi_payments_df WHERE warehouse_id > 93\")\n", "labels": {"reads": [{"table": "bi.bi_payments_df", "columns": ["warehouse_id", "maintenance_contract_id", "hours_billed", "therapeutic_area"]}], "writes": [{"table": "daily_oil_production", "columns": ["warehouse_id", "maintenance_contract_id", "hours_billed", "therapeutic_area"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO railway (gas_fee, fair_trade) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "railway", "columns": ["gas_fee", "fair_trade"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO circuits SELECT a.coverage_type, b.incident_date FROM cargos a JOIN landfills b ON a.trackid = b.trackid\"\n", "labels": {"reads": [{"table": "cargos", "columns": null}, {"table": "landfills", "columns": null}], "writes": [{"table": "circuits", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT long, statename FROM portfolios\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"food_items\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "portfolios", "columns": ["long", "statename"]}], "writes": [{"table": "food_items", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dispensaries --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dispensaries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM diversification_projects\", conn)\ndf.to_sql(\"furniture\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "diversification_projects", "columns": null}], "writes": [{"table": "furniture", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO genderdistribution SELECT budgetid, individual_last_name FROM stg.stg_coupon_use_hourly WHERE budgetid > 260\");\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_hourly", "columns": ["budgetid", "individual_last_name"]}], "writes": [{"table": "genderdistribution", "columns": ["budgetid", "individual_last_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"department_stores\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "department_stores", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO social_impact_bonds SELECT program_name, publication_id, inspectionscore, phone_number FROM permit WHERE program_name > 325\")\n", "labels": {"reads": [{"table": "permit", "columns": ["program_name", "publication_id", "inspectionscore", "phone_number"]}], "writes": [{"table": "social_impact_bonds", "columns": ["program_name", "publication_id", "inspectionscore", "phone_number"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"community_engagement\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"algorithmic_fairness_incidents_monthly\")\n", "labels": {"reads": [{"table": "community_engagement", "columns": null}], "writes": [{"table": "algorithmic_fairness_incidents_monthly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO drought_impact SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.customer_address_id > 210).all()\n# src table: state_info\nengine.execute(\"INSERT INTO regional_archaeologists SELECT * FROM state_info\")\n", "labels": {"reads": [{"table": "state_info", "columns": null}], "writes": [{"table": "regional_archaeologists", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO peakhours SELECT laborproductivity, negative, workshop_id, type_of_thing_code FROM paris_real_estate WHERE laborproductivity > 147\"\n", "labels": {"reads": [{"table": "paris_real_estate", "columns": ["laborproductivity", "negative", "workshop_id", "type_of_thing_code"]}], "writes": [{"table": "peakhours", "columns": ["laborproductivity", "negative", "workshop_id", "type_of_thing_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT completion_status, aid FROM communityevents LIMIT 183\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "communityevents", "columns": ["completion_status", "aid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table climate_communication --columns tree_species,lanes --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "climate_communication", "columns": ["tree_species", "lanes"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 186;\nEOF\n", "labels": {"reads": [{"table": "water_sources", "columns": ["tourists", "home_team_points", "policytype", "building_full_name"]}], "writes": [{"table": "electricvehicles", "columns": ["tourists", "home_team_points", "policytype", "building_full_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT international_passengers, dispensary_name FROM enzyme\", engine)\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"dorm\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "enzyme", "columns": ["international_passengers", "dispensary_name"]}], "writes": [{"table": "dorm", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO staff_department_assignments SELECT affirmative, max_temperature_f, wins, excavationid FROM ods.products_hourly WHERE affirmative > 215\"\n", "labels": {"reads": [{"table": "ods.products_hourly", "columns": ["affirmative", "max_temperature_f", "wins", "excavationid"]}], "writes": [{"table": "staff_department_assignments", "columns": ["affirmative", "max_temperature_f", "wins", "excavationid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT menuname, participated_in_open_pedagogy FROM musicgenre LIMIT 157\")\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO bi.events_df SELECT project_id, operating_system, transact_count FROM customer_month WHERE project_id > 280\")\n", "labels": {"reads": [{"table": "musicgenre", "columns": ["menuname", "participated_in_open_pedagogy"]}, {"table": "customer_month", "columns": ["project_id", "operating_system", "transact_count"]}], "writes": [{"table": "bi.events_df", "columns": ["project_id", "operating_system", "transact_count"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO field SELECT * FROM legacy\ncur.execute(\"SELECT health_equity_metric_2, building_address FROM healthcare LIMIT 97\")\n", "labels": {"reads": [{"table": "healthcare", "columns": ["health_equity_metric_2", "building_address"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM innovation_projects\", conn)\ndf.to_sql(\"temperatureanomalies\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "innovation_projects", "columns": null}], "writes": [{"table": "temperatureanomalies", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model candidates depends on iot_sensors\ndbt build -s candidates --vars '{\"source_table\":\"iot_sensors\"}'\n", "labels": {"reads": [{"table": "iot_sensors", "columns": null}], "writes": [{"table": "candidates", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO train_station SELECT * FROM legacy\nspark.sql(\"INSERT INTO screen_mode SELECT contract_amount, ngo_name, tourists, catalog_level_number FROM ods_products_delta WHERE contract_amount > 203\")\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": ["contract_amount", "ngo_name", "tourists", "catalog_level_number"]}], "writes": [{"table": "screen_mode", "columns": ["contract_amount", "ngo_name", "tourists", "catalog_level_number"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"chemical_production_3\")\nsrc.write.insertInto(\"soil_moisture\", overwrite=True)\n", "labels": {"reads": [{"table": "chemical_production_3", "columns": null}], "writes": [{"table": "soil_moisture", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"shariah_compliant_loans\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "shariah_compliant_loans", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO broadband_providers SELECT awards, effort_name FROM mart.shipments_full WHERE awards > 361\"\n", "labels": {"reads": [{"table": "mart.shipments_full", "columns": ["awards", "effort_name"]}], "writes": [{"table": "broadband_providers", "columns": ["awards", "effort_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO vaccine_administered SELECT * FROM legacy\nspark.sql(\"INSERT INTO conservation_programs SELECT threats, founder_lgbtq FROM airport WHERE threats > 197\")\n", "labels": {"reads": [{"table": "airport", "columns": ["threats", "founder_lgbtq"]}], "writes": [{"table": "conservation_programs", "columns": ["threats", "founder_lgbtq"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibitionsartworks\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT royal_family_id, has_access FROM playergamehistory LIMIT 185\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "playergamehistory", "columns": ["royal_family_id", "has_access"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"stg.coupon_use_delta\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "stg.coupon_use_delta", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"shariah_compliant_loans\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "shariah_compliant_loans", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO conservation SELECT * FROM legacy\nspark.sql(\"INSERT INTO soil_moisture SELECT duration_ms, material_type, resource_name FROM faculty_participates_in WHERE duration_ms > 151\")\n", "labels": {"reads": [{"table": "faculty_participates_in", "columns": ["duration_ms", "material_type", "resource_name"]}], "writes": [{"table": "soil_moisture", "columns": ["duration_ms", "material_type", "resource_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"researchpapers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "researchpapers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_risk_score_df SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO program SELECT genderid, grantid, date_of_notes, clubname FROM ods.vendors_di WHERE genderid > 296\"\n", "labels": {"reads": [{"table": "ods.vendors_di", "columns": ["genderid", "grantid", "date_of_notes", "clubname"]}], "writes": [{"table": "program", "columns": ["genderid", "grantid", "date_of_notes", "clubname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"autonomousvehicleaccidents\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "autonomousvehicleaccidents", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 306;\nEOF\n", "labels": {"reads": [{"table": "atlantic_plate", "columns": ["trip_type", "city_town", "host_id", "other_hotel_details"]}], "writes": [{"table": "missions", "columns": ["trip_type", "city_town", "host_id", "other_hotel_details"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT is_ev, velocity FROM green_energy_lending_programs LIMIT 12\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO mart.mart_coupon_use_full SELECT vegan, completed FROM conservation_initiatives WHERE vegan > 368\")\n", "labels": {"reads": [{"table": "green_energy_lending_programs", "columns": ["is_ev", "velocity"]}, {"table": "conservation_initiatives", "columns": ["vegan", "completed"]}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": ["vegan", "completed"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"attendance\")\nsrc.write.insertInto(\"watertreatmentplants\", overwrite=True)\n", "labels": {"reads": [{"table": "attendance", "columns": null}], "writes": [{"table": "watertreatmentplants", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ethical_manufacturing > 55).all()\n# src table: socialimpactinvestments\nengine.execute(\"INSERT INTO stg.inventory_df SELECT * FROM socialimpactinvestments\")\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": [{"table": "stg.inventory_df", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.users SELECT allergy, numcases, dob FROM intelligence_personnel WHERE allergy > 141\"\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": ["allergy", "numcases", "dob"]}], "writes": [{"table": "stg.users", "columns": ["allergy", "numcases", "dob"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO loan SELECT crispr_id, other_account_details FROM ticket_sales WHERE crispr_id > 327\"\n", "labels": {"reads": [{"table": "ticket_sales", "columns": ["crispr_id", "other_account_details"]}], "writes": [{"table": "loan", "columns": ["crispr_id", "other_account_details"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.mart_refunds_hourly SELECT scientist, dorm_name, writer FROM field5 WHERE scientist > 175\"], check=True)\n", "labels": {"reads": [{"table": "field5", "columns": ["scientist", "dorm_name", "writer"]}], "writes": [{"table": "mart.mart_refunds_hourly", "columns": ["scientist", "dorm_name", "writer"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO conservation_projects SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO nursing_homes SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.park_id > 25).all()\n# src table: students_enrollment\nengine.execute(\"INSERT INTO forests SELECT * FROM students_enrollment\")\n", "labels": {"reads": [{"table": "students_enrollment", "columns": null}], "writes": [{"table": "forests", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table streams --columns player_id,cargoid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "streams", "columns": ["player_id", "cargoid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO design_standards SELECT platform_id, is_false, species_id, delegate FROM zipcodes WHERE platform_id > 162\");\n", "labels": {"reads": [{"table": "zipcodes", "columns": ["platform_id", "is_false", "species_id", "delegate"]}], "writes": [{"table": "design_standards", "columns": ["platform_id", "is_false", "species_id", "delegate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM hotel_ratings\"\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ods.ods_sessions_df SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO systems SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table chemical_processes --columns bikes_available,chip_model --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "chemical_processes", "columns": ["bikes_available", "chip_model"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ads.users_full\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"broadband_plans\")\n", "labels": {"reads": [{"table": "ads.users_full", "columns": null}], "writes": [{"table": "broadband_plans", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"constructors\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"haircare_cruelty\")\n", "labels": {"reads": [{"table": "constructors", "columns": null}], "writes": [{"table": "haircare_cruelty", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO news_views SELECT inventoryid, appointment_date, indigenous, concert_id FROM genetic.projects WHERE inventoryid > 446\"\n", "labels": {"reads": [{"table": "genetic.projects", "columns": ["inventoryid", "appointment_date", "indigenous", "concert_id"]}], "writes": [{"table": "news_views", "columns": ["inventoryid", "appointment_date", "indigenous", "concert_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO prices SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO recalls SELECT working_year_starts, billing_city FROM employeedemographics WHERE working_year_starts > 276\")\n", "labels": {"reads": [{"table": "employeedemographics", "columns": ["working_year_starts", "billing_city"]}], "writes": [{"table": "recalls", "columns": ["working_year_starts", "billing_city"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO training SELECT vehicle_details, attendee_race, system, casestatus FROM bike_share WHERE vehicle_details > 127\"\n", "labels": {"reads": [{"table": "bike_share", "columns": ["vehicle_details", "attendee_race", "system", "casestatus"]}], "writes": [{"table": "training", "columns": ["vehicle_details", "attendee_race", "system", "casestatus"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 103;\nSQL\n", "labels": {"reads": [{"table": "biomes", "columns": ["fleet_series", "strat_id"]}, {"table": "drilling_rigs", "columns": ["vendor_name", "nominee", "style", "diversity_score"]}], "writes": [{"table": "timbersales", "columns": ["vendor_name", "nominee", "style", "diversity_score"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO submersible_dives SELECT hometeam, mouse_id FROM department_store_chain WHERE hometeam > 139\"\n", "labels": {"reads": [{"table": "department_store_chain", "columns": ["hometeam", "mouse_id"]}], "writes": [{"table": "submersible_dives", "columns": ["hometeam", "mouse_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.payments_daily (union_name, menu_item) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.payments_daily", "columns": ["union_name", "menu_item"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"fishcaught\")\nsrc.write.insertInto(\"sports_events\", overwrite=True)\n", "labels": {"reads": [{"table": "fishcaught", "columns": null}], "writes": [{"table": "sports_events", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dw.exposure_di SELECT visit_id, restaurant_name, vesselid, signupdate FROM dw.dw_sessions_delta WHERE visit_id > 87\"\n", "labels": {"reads": [{"table": "dw.dw_sessions_delta", "columns": ["visit_id", "restaurant_name", "vesselid", "signupdate"]}], "writes": [{"table": "dw.exposure_di", "columns": ["visit_id", "restaurant_name", "vesselid", "signupdate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mountain SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthydelights\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "healthydelights", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 416;\nEOF\n", "labels": {"reads": [{"table": "militaryoperations", "columns": ["actor_name", "institution", "ship_date"]}], "writes": [{"table": "ads_orders", "columns": ["actor_name", "institution", "ship_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ticket_sales (treasurer_vote, completion_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ticket_sales", "columns": ["treasurer_vote", "completion_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_events_di\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"midwest_materials\")\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": null}], "writes": [{"table": "midwest_materials", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO healthcare_budget SELECT precedent_id, count_id, emp_hiredate FROM dws.dws_risk_score_daily WHERE precedent_id > 126\")\n", "labels": {"reads": [{"table": "dws.dws_risk_score_daily", "columns": ["precedent_id", "count_id", "emp_hiredate"]}], "writes": [{"table": "healthcare_budget", "columns": ["precedent_id", "count_id", "emp_hiredate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO fairtradefactories SELECT investor_id, trade, contractor_id, is_hybrid FROM ads.ads_products_full WHERE investor_id > 452\");\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": ["investor_id", "trade", "contractor_id", "is_hybrid"]}], "writes": [{"table": "fairtradefactories", "columns": ["investor_id", "trade", "contractor_id", "is_hybrid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"maintenance_requests\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"donorprograms\")\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": null}], "writes": [{"table": "donorprograms", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO musical SELECT project_name, device_id, quantity_sold FROM wastewater_plants WHERE project_name > 416\")\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": ["project_name", "device_id", "quantity_sold"]}], "writes": [{"table": "musical", "columns": ["project_name", "device_id", "quantity_sold"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO textile_sourcing SELECT * FROM legacy\ncur.execute(\"SELECT retailer_name, last_updated FROM bi.events_delta LIMIT 383\")\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": ["retailer_name", "last_updated"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"threats\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "threats", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO procedures SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT part_id, session_language FROM carbon_prices\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nimport logging\ndf.to_sql(\"labor_unions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "carbon_prices", "columns": ["part_id", "session_language"]}], "writes": [{"table": "labor_unions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotels\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hotels", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 225;\nEOF\n", "labels": {"reads": [{"table": "measurements", "columns": ["visit_id", "donortype", "class_section"]}], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": ["visit_id", "donortype", "class_section"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws_events_df\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dws_events_df", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT prod_id, spacecraft_model FROM employeedemographics\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"volunteer_hours\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "employeedemographics", "columns": ["prod_id", "spacecraft_model"]}], "writes": [{"table": "volunteer_hours", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"healthcare_centers\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dws.dws_shipments_full SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO higher_ed.students SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO displaced_people SELECT airport, testdate FROM farm_competition WHERE airport > 497\"\n", "labels": {"reads": [{"table": "farm_competition", "columns": ["airport", "testdate"]}], "writes": [{"table": "displaced_people", "columns": ["airport", "testdate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model malicious_activity depends on sustainableproduction\ndbt build --models malicious_activity --vars 'source: sustainableproduction'\n", "labels": {"reads": [{"table": "sustainableproduction", "columns": null}], "writes": [{"table": "malicious_activity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"salinity_readings\").where(\"dt = current_date()\").writeTo(\"lives_in\").append()\n", "labels": {"reads": [{"table": "salinity_readings", "columns": null}], "writes": [{"table": "lives_in", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO drivers SELECT archaeologist_name, handling_id FROM defense_contracts WHERE archaeologist_name > 190\"\n", "labels": {"reads": [{"table": "defense_contracts", "columns": ["archaeologist_name", "handling_id"]}], "writes": [{"table": "drivers", "columns": ["archaeologist_name", "handling_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO conservation_programs SELECT well_id, area_name, updatedate FROM sustainable_projects WHERE well_id > 212\");\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["well_id", "area_name", "updatedate"]}], "writes": [{"table": "conservation_programs", "columns": ["well_id", "area_name", "updatedate"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO prereq SELECT * FROM legacy\ncur.execute(\"SELECT union_member_id, site_name FROM neodymium_prices LIMIT 209\")\n", "labels": {"reads": [{"table": "neodymium_prices", "columns": ["union_member_id", "site_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT arrival_date, team_name FROM researchpapers LIMIT 73\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO participates_in SELECT society, market_id FROM stores_2 WHERE society > 46\")\n", "labels": {"reads": [{"table": "researchpapers", "columns": ["arrival_date", "team_name"]}, {"table": "stores_2", "columns": ["society", "market_id"]}], "writes": [{"table": "participates_in", "columns": ["society", "market_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO customers_policies SELECT * FROM legacy\ncur.execute(\"SELECT participant_name, heritage_site FROM lives_in LIMIT 15\")\n", "labels": {"reads": [{"table": "lives_in", "columns": ["participant_name", "heritage_site"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 445;\nEOF\n", "labels": {"reads": [{"table": "discipline_enrollments", "columns": ["mineral", "is_vegan", "stop"]}], "writes": [{"table": "aquatic_species", "columns": ["mineral", "is_vegan", "stop"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vaccinations\").toPandas()\ndf[[\"personnelid\", \"password\"]].to_sql(\"hospital_visits\", engine, index=False)\n", "labels": {"reads": [{"table": "vaccinations", "columns": null}], "writes": [{"table": "hospital_visits", "columns": ["personnelid", "password"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dapps\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"esportsteamsafrica\")\n", "labels": {"reads": [{"table": "dapps", "columns": null}], "writes": [{"table": "esportsteamsafrica", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO measurements SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO donations_insert_2 SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM public_schools\"\n", "labels": {"reads": [{"table": "public_schools", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.refunds SELECT maintenance_id, matchdate FROM field4_precip WHERE maintenance_id > 167\"\n", "labels": {"reads": [{"table": "field4_precip", "columns": ["maintenance_id", "matchdate"]}], "writes": [{"table": "ads.refunds", "columns": ["maintenance_id", "matchdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart_events_full --columns hub_id,fleet_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart_events_full", "columns": ["hub_id", "fleet_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO labels SELECT user_id, raceid FROM dws.dws_member_point_di WHERE user_id > 33\")\n", "labels": {"reads": [{"table": "dws.dws_member_point_di", "columns": ["user_id", "raceid"]}], "writes": [{"table": "labels", "columns": ["user_id", "raceid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO policyadvocacyevents SELECT region_code, advisoryid, technique FROM marine_life_research WHERE region_code > 216\");\n", "labels": {"reads": [{"table": "marine_life_research", "columns": ["region_code", "advisoryid", "technique"]}], "writes": [{"table": "policyadvocacyevents", "columns": ["region_code", "advisoryid", "technique"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT state_province_county, track_id FROM university LIMIT 258\")\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO casesbyyear SELECT carbon_footprint, job_title, day_of_week, claimid FROM rural_clinics WHERE carbon_footprint > 183\")\n", "labels": {"reads": [{"table": "university", "columns": ["state_province_county", "track_id"]}, {"table": "rural_clinics", "columns": ["carbon_footprint", "job_title", "day_of_week", "claimid"]}], "writes": [{"table": "casesbyyear", "columns": ["carbon_footprint", "job_title", "day_of_week", "claimid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table equipment --columns trend,protein_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "equipment", "columns": ["trend", "protein_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 149;\nEOF\n", "labels": {"reads": [{"table": "debris", "columns": ["ticket_price", "bill_id"]}], "writes": [{"table": "employment", "columns": ["ticket_price", "bill_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"inclusivehousingpolicies\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "inclusivehousingpolicies", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO hotel_business_partnerships SELECT * FROM legacy\ncur.execute(\"SELECT contractorname, consumer_id FROM clinics LIMIT 116\")\n", "labels": {"reads": [{"table": "clinics", "columns": ["contractorname", "consumer_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart_campaigns_delta\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mart_campaigns_delta", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 326;\nSQL\n", "labels": {"reads": [{"table": "financial_capability", "columns": ["programname", "participation_date"]}, {"table": "articles_es", "columns": ["home_team_points", "communityname", "classtype"]}], "writes": [{"table": "audience_demographics", "columns": ["home_team_points", "communityname", "classtype"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT virtual_tour_engagement_time, name_last FROM mart.shipments_df\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"hospital\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart.shipments_df", "columns": ["virtual_tour_engagement_time", "name_last"]}], "writes": [{"table": "hospital", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO cultural_events SELECT * FROM legacy\nspark.sql(\"INSERT INTO cuisine SELECT card_type_code, university_type, regulation FROM tourism WHERE card_type_code > 264\")\n", "labels": {"reads": [{"table": "tourism", "columns": ["card_type_code", "university_type", "regulation"]}], "writes": [{"table": "cuisine", "columns": ["card_type_code", "university_type", "regulation"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"coralreefs\").toPandas()\ndf[[\"farmid\", \"doctorsper1000\"]].to_sql(\"menu_item\", engine, index=False)\n", "labels": {"reads": [{"table": "coralreefs", "columns": null}], "writes": [{"table": "menu_item", "columns": ["farmid", "doctorsper1000"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO excavation_sites (title, culture) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "excavation_sites", "columns": ["title", "culture"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO member_data SELECT * FROM legacy\ncur.execute(\"SELECT passenger_name, negotiation_date FROM genetics.experiments LIMIT 158\")\n", "labels": {"reads": [{"table": "genetics.experiments", "columns": ["passenger_name", "negotiation_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO labels SELECT card_type_code, thefttype FROM dws.inventory_daily WHERE card_type_code > 245\")\n", "labels": {"reads": [{"table": "dws.inventory_daily", "columns": ["card_type_code", "thefttype"]}], "writes": [{"table": "labels", "columns": ["card_type_code", "thefttype"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO vessel_incident_count SELECT starting_year, site_name, warehousename FROM user_interests WHERE starting_year > 191\")\n", "labels": {"reads": [{"table": "user_interests", "columns": ["starting_year", "site_name", "warehousename"]}], "writes": [{"table": "vessel_incident_count", "columns": ["starting_year", "site_name", "warehousename"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO products_in_events SELECT num_of_component, transaction_date FROM dws.dws_risk_score_daily WHERE num_of_component > 357\")\n", "labels": {"reads": [{"table": "dws.dws_risk_score_daily", "columns": ["num_of_component", "transaction_date"]}], "writes": [{"table": "products_in_events", "columns": ["num_of_component", "transaction_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT policy_count, regulation FROM category_revenue LIMIT 328\")\nrows = cur.fetchall()\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "category_revenue", "columns": ["policy_count", "regulation"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM voting_data\"\n", "labels": {"reads": [{"table": "voting_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table creative_ai --columns unionid,studio --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "creative_ai", "columns": ["unionid", "studio"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO sportsinfo SELECT * FROM legacy\nspark.sql(\"INSERT INTO organisations SELECT cause_name, stayid FROM fish_feed_factories WHERE cause_name > 341\")\n", "labels": {"reads": [{"table": "fish_feed_factories", "columns": ["cause_name", "stayid"]}], "writes": [{"table": "organisations", "columns": ["cause_name", "stayid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.refunds SELECT price, countryname, product_id FROM landfillcapacitybycountry WHERE price > 71\"\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": ["price", "countryname", "product_id"]}], "writes": [{"table": "stg.refunds", "columns": ["price", "countryname", "product_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sales_region\", conn)\ndf.to_sql(\"parks\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sales_region", "columns": null}], "writes": [{"table": "parks", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vesselarrivals\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"num_employees\")\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": null}], "writes": [{"table": "num_employees", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fields\").toPandas()\ndf[[\"cust_id\", \"community_center_id\"]].to_sql(\"trainingprograms\", engine, index=False)\n", "labels": {"reads": [{"table": "fields", "columns": null}], "writes": [{"table": "trainingprograms", "columns": ["cust_id", "community_center_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.contractorid > 304).all()\n# src table: faculty\nengine.execute(\"INSERT INTO threat_intel SELECT * FROM faculty\")\n", "labels": {"reads": [{"table": "faculty", "columns": null}], "writes": [{"table": "threat_intel", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT competition_type, rig_name FROM mart_refunds LIMIT 149\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO disease_prevalence SELECT founder_ethnicity, ai_adoption FROM regulatory_compliance WHERE founder_ethnicity > 310\")\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["competition_type", "rig_name"]}, {"table": "regulatory_compliance", "columns": ["founder_ethnicity", "ai_adoption"]}], "writes": [{"table": "disease_prevalence", "columns": ["founder_ethnicity", "ai_adoption"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model cargo_handling depends on renewable_power\ndbt build --select cargo_handling --vars 'source: renewable_power'\n", "labels": {"reads": [{"table": "renewable_power", "columns": null}], "writes": [{"table": "cargo_handling", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO legal_aid_organizations SELECT college_location, musical_id FROM seafoodsouthafricakenya WHERE college_location > 453\")\n", "labels": {"reads": [{"table": "seafoodsouthafricakenya", "columns": ["college_location", "musical_id"]}], "writes": [{"table": "legal_aid_organizations", "columns": ["college_location", "musical_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"weather_record\").where(\"dt = current_date()\").writeTo(\"whale_sightings\").append()\n", "labels": {"reads": [{"table": "weather_record", "columns": null}], "writes": [{"table": "whale_sightings", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 248;\nSQL\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": ["assessmentdate", "claimid"]}, {"table": "budget", "columns": ["dispensary", "song_id", "price_in_euros"]}], "writes": [{"table": "device_accessibility", "columns": ["dispensary", "song_id", "price_in_euros"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"hotel_business_partnerships\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"skincaresales\")\n", "labels": {"reads": [{"table": "hotel_business_partnerships", "columns": null}], "writes": [{"table": "skincaresales", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO timbersales SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"artist_data\").toPandas()\ndf[[\"phone_id\", \"item_id\"]].to_sql(\"daily_industrial_water_usage\", engine, index=False)\n", "labels": {"reads": [{"table": "artist_data", "columns": null}], "writes": [{"table": "daily_industrial_water_usage", "columns": ["phone_id", "item_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table mart.device_log_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.device_log_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"electronics_factories\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "electronics_factories", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO excavation SELECT extractiondate, updated_at, program FROM chemical_production_5 WHERE extractiondate > 171\")\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": ["extractiondate", "updated_at", "program"]}], "writes": [{"table": "excavation", "columns": ["extractiondate", "updated_at", "program"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO streams SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"solana_transactions\")\nsrc.write.insertInto(\"dw.shipments_df\", overwrite=True)\n", "labels": {"reads": [{"table": "solana_transactions", "columns": null}], "writes": [{"table": "dw.shipments_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ma_inspections depends on movie_ratings\ndbt run --select ma_inspections --vars 'source: movie_ratings'\n", "labels": {"reads": [{"table": "movie_ratings", "columns": null}], "writes": [{"table": "ma_inspections", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model constructionlaborstatistics depends on strategies\ndbt build -s constructionlaborstatistics --vars '{\"source_table\":\"strategies\"}'\n", "labels": {"reads": [{"table": "strategies", "columns": null}], "writes": [{"table": "constructionlaborstatistics", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO marketingbudget SELECT authorder, is_sustainable, energy_efficiency_rating FROM functional_areas WHERE authorder > 100\")\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["authorder", "is_sustainable", "energy_efficiency_rating"]}], "writes": [{"table": "marketingbudget", "columns": ["authorder", "is_sustainable", "energy_efficiency_rating"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table council_tax --target-dir /tmp/land\n", "labels": {"reads": [{"table": "council_tax", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"clothingsales\").where(\"dt = current_date()\").writeTo(\"budget_allocations\").append()\n", "labels": {"reads": [{"table": "clothingsales", "columns": null}], "writes": [{"table": "budget_allocations", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO ota_revenue SELECT image_data, excavation_site_id FROM authorship WHERE image_data > 296\")\n", "labels": {"reads": [{"table": "authorship", "columns": ["image_data", "excavation_site_id"]}], "writes": [{"table": "ota_revenue", "columns": ["image_data", "excavation_site_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO marinespeciesobservations SELECT * FROM legacy\ncur.execute(\"SELECT sales_in_billion, coverage_type FROM dw_risk_score_daily LIMIT 276\")\n", "labels": {"reads": [{"table": "dw_risk_score_daily", "columns": ["sales_in_billion", "coverage_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO course_authors_and_tutors SELECT game, policy_id FROM intelligence_agents WHERE game > 131\")\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": ["game", "policy_id"]}], "writes": [{"table": "course_authors_and_tutors", "columns": ["game", "policy_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"safety_incident\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "safety_incident", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT card_type_code, camera_lens_id FROM bi_orders_daily\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"ads.ads_payments_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bi_orders_daily", "columns": ["card_type_code", "camera_lens_id"]}], "writes": [{"table": "ads.ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT labordate, operationid FROM plants LIMIT 500\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO events SELECT dock_status, checkout, transaction_type_description, spending FROM fleets WHERE dock_status > 329\")\n", "labels": {"reads": [{"table": "plants", "columns": ["labordate", "operationid"]}, {"table": "fleets", "columns": ["dock_status", "checkout", "transaction_type_description", "spending"]}], "writes": [{"table": "events", "columns": ["dock_status", "checkout", "transaction_type_description", "spending"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd.vendors --columns workers,deliveryid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd.vendors", "columns": ["workers", "deliveryid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.dws_campaigns_df SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM customer_address_history\", conn)\ndf.to_sql(\"ads_payments_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "customer_address_history", "columns": null}], "writes": [{"table": "ads_payments_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table airportdata --target-dir /tmp/land\n", "labels": {"reads": [{"table": "airportdata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO field4_precip SELECT platform, loadingstart, store_id FROM marine_life_populations WHERE platform > 23\")\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": ["platform", "loadingstart", "store_id"]}], "writes": [{"table": "field4_precip", "columns": ["platform", "loadingstart", "store_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO supportservices SELECT dispensary_id, pilot, group_equity_shareholding, other_hotel_details FROM field4_precip WHERE dispensary_id > 22\");\n", "labels": {"reads": [{"table": "field4_precip", "columns": ["dispensary_id", "pilot", "group_equity_shareholding", "other_hotel_details"]}], "writes": [{"table": "supportservices", "columns": ["dispensary_id", "pilot", "group_equity_shareholding", "other_hotel_details"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO vessel_incident_count SELECT amount_due, height, contract_id, policy_count FROM storage_tech WHERE amount_due > 355\");\n", "labels": {"reads": [{"table": "storage_tech", "columns": ["amount_due", "height", "contract_id", "policy_count"]}], "writes": [{"table": "vessel_incident_count", "columns": ["amount_due", "height", "contract_id", "policy_count"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO org_volunteer SELECT video_id, network, bank_name, users_engaged FROM ethicalaibudget WHERE video_id > 496\")\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": ["video_id", "network", "bank_name", "users_engaged"]}], "writes": [{"table": "org_volunteer", "columns": ["video_id", "network", "bank_name", "users_engaged"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_table(ctx, \"behavior_incident\")\nsave_to_warehouse(df, \"dwd_coupon_use_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "behavior_incident", "columns": null}], "writes": [{"table": "dwd_coupon_use_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.contact_staff_id > 381).all()\n# src table: dws.dws_clicks_full\nengine.execute(\"INSERT INTO ads_users_hourly SELECT * FROM dws.dws_clicks_full\")\n", "labels": {"reads": [{"table": "dws.dws_clicks_full", "columns": null}], "writes": [{"table": "ads_users_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO station_emergencies SELECT financial_capability_score, spf_level, reported_date, base_name FROM mart.mart_payments_df WHERE financial_capability_score > 107\")\n", "labels": {"reads": [{"table": "mart.mart_payments_df", "columns": ["financial_capability_score", "spf_level", "reported_date", "base_name"]}], "writes": [{"table": "station_emergencies", "columns": ["financial_capability_score", "spf_level", "reported_date", "base_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"teacher_development_race\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"document_structures\")\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": null}], "writes": [{"table": "document_structures", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"life_expectancy\").toPandas()\ndf[[\"founder_count\", \"shipmenttype\"]].to_sql(\"sustainabilityratings\", engine, index=False)\n", "labels": {"reads": [{"table": "life_expectancy", "columns": null}], "writes": [{"table": "sustainabilityratings", "columns": ["founder_count", "shipmenttype"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_events_delta\").toPandas()\ndf[[\"carrierid\", \"destination\"]].to_sql(\"volunteer_signups\", engine, index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": null}], "writes": [{"table": "volunteer_signups", "columns": ["carrierid", "destination"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.salinity > 400).all()\n# src table: hair_care_sales\nengine.execute(\"INSERT INTO community_engagement SELECT * FROM hair_care_sales\")\n", "labels": {"reads": [{"table": "hair_care_sales", "columns": null}], "writes": [{"table": "community_engagement", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM job_postings\"\n", "labels": {"reads": [{"table": "job_postings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model customer_transactions depends on voyages\ndbt run -s customer_transactions --vars '{\"source_table\":\"voyages\"}'\n", "labels": {"reads": [{"table": "voyages", "columns": null}], "writes": [{"table": "customer_transactions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"production_costs\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "production_costs", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model recycling_centers depends on community_leaders\ndbt run --models recycling_centers --vars 'source: community_leaders'\n", "labels": {"reads": [{"table": "community_leaders", "columns": null}], "writes": [{"table": "recycling_centers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO tours SELECT shipped_to, policytype FROM plots WHERE shipped_to > 300\")\n", "labels": {"reads": [{"table": "plots", "columns": ["shipped_to", "policytype"]}], "writes": [{"table": "tours", "columns": ["shipped_to", "policytype"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bioprocess.engineering_projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bioprocess.engineering_projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sustainability_initiatives SELECT a.dept_name, b.restock_date FROM district a JOIN parking_fines b ON a.name_full = b.name_full\"\n", "labels": {"reads": [{"table": "district", "columns": null}, {"table": "parking_fines", "columns": null}], "writes": [{"table": "sustainability_initiatives", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"artcontributors\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "artcontributors", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO team_revenue SELECT organic_ingredients_percentage, members, order_item_status, show_id FROM crime_incidents WHERE organic_ingredients_percentage > 174\")\n", "labels": {"reads": [{"table": "crime_incidents", "columns": ["organic_ingredients_percentage", "members", "order_item_status", "show_id"]}], "writes": [{"table": "team_revenue", "columns": ["organic_ingredients_percentage", "members", "order_item_status", "show_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.ods_risk_score_df\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO electric_buses SELECT personnel, vegan, last_checkup_date FROM ref_incident_type WHERE personnel > 23\");\n", "labels": {"reads": [{"table": "ref_incident_type", "columns": ["personnel", "vegan", "last_checkup_date"]}], "writes": [{"table": "electric_buses", "columns": ["personnel", "vegan", "last_checkup_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"has_allergy\")\nsrc.write.insertInto(\"publications\", overwrite=True)\n", "labels": {"reads": [{"table": "has_allergy", "columns": null}], "writes": [{"table": "publications", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO customer_events SELECT a.sample_date, b.violation_type FROM dwd.dwd_exposure_full a JOIN dancefunding b ON a.element = b.element\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_full", "columns": null}, {"table": "dancefunding", "columns": null}], "writes": [{"table": "customer_events", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO dw.dw_orders_hourly SELECT eventid, architect_id, vrdevice FROM menu_engineering WHERE eventid > 475\");\n", "labels": {"reads": [{"table": "menu_engineering", "columns": ["eventid", "architect_id", "vrdevice"]}], "writes": [{"table": "dw.dw_orders_hourly", "columns": ["eventid", "architect_id", "vrdevice"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart.clicks SELECT a.hosts, b.activity FROM bus_fare_collection a JOIN all_documents b ON a.contract_type = b.contract_type\"\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": null}, {"table": "all_documents", "columns": null}], "writes": [{"table": "mart.clicks", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"volunteer_signups\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"temperaturehistory\")\n", "labels": {"reads": [{"table": "volunteer_signups", "columns": null}], "writes": [{"table": "temperaturehistory", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT production_mwh, rainfall FROM government.region LIMIT 493\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "government.region", "columns": ["production_mwh", "rainfall"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO concert_events SELECT mineid, thefttype, model_name, draft_details FROM intelligence_agents WHERE mineid > 196\"], check=True)\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": ["mineid", "thefttype", "model_name", "draft_details"]}], "writes": [{"table": "concert_events", "columns": ["mineid", "thefttype", "model_name", "draft_details"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO regional_railways SELECT a.inspectionscore, b.onscholarship FROM buildings a JOIN food_safety_inspections b ON a.browser_id = b.browser_id\"\n", "labels": {"reads": [{"table": "buildings", "columns": null}, {"table": "food_safety_inspections", "columns": null}], "writes": [{"table": "regional_railways", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"section\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"fabrics\")\n", "labels": {"reads": [{"table": "section", "columns": null}], "writes": [{"table": "fabrics", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM product_reviews\", conn)\ndf.to_sql(\"apartment_bookings\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "product_reviews", "columns": null}], "writes": [{"table": "apartment_bookings", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT veteran_id, investorgender FROM hospital_equipment LIMIT 480\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "hospital_equipment", "columns": ["veteran_id", "investorgender"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 311;\nEOF\n", "labels": {"reads": [{"table": "premises", "columns": ["num_accessible_tech_centers", "eventtype", "document_status_code", "transaction_type_description"]}], "writes": [{"table": "local_impact_japan", "columns": ["num_accessible_tech_centers", "eventtype", "document_status_code", "transaction_type_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT check_in_date, rehab_date FROM mart_events_full\", engine)\nimport logging\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"dwd.exposure_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart_events_full", "columns": ["check_in_date", "rehab_date"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO habitat SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.incident_name > 356).all()\n# src table: cargo_data\nengine.execute(\"INSERT INTO esportsteamsafrica SELECT * FROM cargo_data\")\n", "labels": {"reads": [{"table": "cargo_data", "columns": null}], "writes": [{"table": "esportsteamsafrica", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO parts SELECT source_system_code, asset_type, zipcode FROM socially_responsible_loans WHERE source_system_code > 402\")\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": ["source_system_code", "asset_type", "zipcode"]}], "writes": [{"table": "parts", "columns": ["source_system_code", "asset_type", "zipcode"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO activities SELECT acc_percent, room_number FROM degrees WHERE acc_percent > 303\"], check=True)\n", "labels": {"reads": [{"table": "degrees", "columns": ["acc_percent", "room_number"]}], "writes": [{"table": "activities", "columns": ["acc_percent", "room_number"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO plants SELECT archaeologist_id, party FROM dwd_risk_score_hourly WHERE archaeologist_id > 196\")\n", "labels": {"reads": [{"table": "dwd_risk_score_hourly", "columns": ["archaeologist_id", "party"]}], "writes": [{"table": "plants", "columns": ["archaeologist_id", "party"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.acc_regular_season > 47).all()\n# src table: host\nengine.execute(\"INSERT INTO bookings SELECT * FROM host\")\n", "labels": {"reads": [{"table": "host", "columns": null}], "writes": [{"table": "bookings", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wastewater_treatment_plants SELECT * FROM legacy\ncur.execute(\"SELECT forest_id, prof_num FROM fish_suppliers LIMIT 386\")\n", "labels": {"reads": [{"table": "fish_suppliers", "columns": ["forest_id", "prof_num"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table communities --target-dir /tmp/land\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT license_plate, distance FROM ingredient_sourcing LIMIT 371\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO shelters SELECT artpiecename, governor, resident_id FROM purchases WHERE artpiecename > 153\")\n", "labels": {"reads": [{"table": "ingredient_sourcing", "columns": ["license_plate", "distance"]}, {"table": "purchases", "columns": ["artpiecename", "governor", "resident_id"]}], "writes": [{"table": "shelters", "columns": ["artpiecename", "governor", "resident_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_input(ctx, \"sales\")\ndump_to_warehouse(df, \"community_policing_events\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "sales", "columns": null}], "writes": [{"table": "community_policing_events", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO agencies SELECT * FROM legacy\nspark.sql(\"INSERT INTO space_agencies_2 SELECT ingredient_name, party, funding_id, fairness_score FROM humanitarian_aid WHERE ingredient_name > 327\")\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": ["ingredient_name", "party", "funding_id", "fairness_score"]}], "writes": [{"table": "space_agencies_2", "columns": ["ingredient_name", "party", "funding_id", "fairness_score"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"production_rare_earth_elements\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "production_rare_earth_elements", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM communication_scores\"\n", "labels": {"reads": [{"table": "communication_scores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO debris SELECT participation_id, communityname, organisation_id, bioprocess_id FROM member_activity WHERE participation_id > 403\"\n", "labels": {"reads": [{"table": "member_activity", "columns": ["participation_id", "communityname", "organisation_id", "bioprocess_id"]}], "writes": [{"table": "debris", "columns": ["participation_id", "communityname", "organisation_id", "bioprocess_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO ads_users_hourly SELECT num_of_component, time_second, shop_id FROM authorship WHERE num_of_component > 153\");\n", "labels": {"reads": [{"table": "authorship", "columns": ["num_of_component", "time_second", "shop_id"]}], "writes": [{"table": "ads_users_hourly", "columns": ["num_of_component", "time_second", "shop_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT text, area FROM ref_budget_codes LIMIT 382\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO support_groups SELECT fan_name, pd_id FROM stg.stg_clicks_delta WHERE fan_name > 79\")\n", "labels": {"reads": [{"table": "ref_budget_codes", "columns": ["text", "area"]}, {"table": "stg.stg_clicks_delta", "columns": ["fan_name", "pd_id"]}], "writes": [{"table": "support_groups", "columns": ["fan_name", "pd_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO low_value_contracts SELECT * FROM legacy\nspark.sql(\"INSERT INTO visualartprograms SELECT club_id, inspectionid, precip, publication_id FROM cotton_source WHERE club_id > 309\")\n", "labels": {"reads": [{"table": "cotton_source", "columns": ["club_id", "inspectionid", "precip", "publication_id"]}], "writes": [{"table": "visualartprograms", "columns": ["club_id", "inspectionid", "precip", "publication_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"passengers\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "passengers", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dwd.dwd_events_delta SELECT goal_date, worker_name, q1_2022_views FROM workplace_safety WHERE goal_date > 162\"\n", "labels": {"reads": [{"table": "workplace_safety", "columns": ["goal_date", "worker_name", "q1_2022_views"]}], "writes": [{"table": "dwd.dwd_events_delta", "columns": ["goal_date", "worker_name", "q1_2022_views"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"climate_mitigation_projects\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"manufacturermaterials\")\n", "labels": {"reads": [{"table": "climate_mitigation_projects", "columns": null}], "writes": [{"table": "manufacturermaterials", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO dws_shipments_df SELECT dorm_name, name_full, receipt_date, size_ha FROM bi.inventory_delta WHERE dorm_name > 14\")\n", "labels": {"reads": [{"table": "bi.inventory_delta", "columns": ["dorm_name", "name_full", "receipt_date", "size_ha"]}], "writes": [{"table": "dws_shipments_df", "columns": ["dorm_name", "name_full", "receipt_date", "size_ha"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table construction_union --columns projecttype,startup_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "construction_union", "columns": ["projecttype", "startup_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"band\")\nsrc.write.insertInto(\"shipmentinfo\", overwrite=True)\n", "labels": {"reads": [{"table": "band", "columns": null}], "writes": [{"table": "shipmentinfo", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO roller_coaster SELECT speed, valuation, observation_id FROM bi.products_daily WHERE speed > 475\");\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": ["speed", "valuation", "observation_id"]}], "writes": [{"table": "roller_coaster", "columns": ["speed", "valuation", "observation_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sectors\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "sectors", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO studies SELECT exhibitionname, last_maintenance, region_name, blockcode FROM states WHERE exhibitionname > 179\"\n", "labels": {"reads": [{"table": "states", "columns": ["exhibitionname", "last_maintenance", "region_name", "blockcode"]}], "writes": [{"table": "studies", "columns": ["exhibitionname", "last_maintenance", "region_name", "blockcode"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT founder_group, practice FROM stations\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"device_accessibility\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stations", "columns": ["founder_group", "practice"]}], "writes": [{"table": "device_accessibility", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hoursperweek, app_name FROM police_officers_tx LIMIT 224\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO fishcaught SELECT school_colors, membergender FROM levees WHERE school_colors > 438\")\n", "labels": {"reads": [{"table": "police_officers_tx", "columns": ["hoursperweek", "app_name"]}, {"table": "levees", "columns": ["school_colors", "membergender"]}], "writes": [{"table": "fishcaught", "columns": ["school_colors", "membergender"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO employee SELECT violation_count, party_theme, amount_due, parent_organization_id FROM program WHERE violation_count > 204\")\n", "labels": {"reads": [{"table": "program", "columns": ["violation_count", "party_theme", "amount_due", "parent_organization_id"]}], "writes": [{"table": "employee", "columns": ["violation_count", "party_theme", "amount_due", "parent_organization_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO forest SELECT excavationid, collection_id, dphone, price FROM daily_industrial_water_usage WHERE excavationid > 53\")\n", "labels": {"reads": [{"table": "daily_industrial_water_usage", "columns": ["excavationid", "collection_id", "dphone", "price"]}], "writes": [{"table": "forest", "columns": ["excavationid", "collection_id", "dphone", "price"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT stateid, menu_id FROM document_sections_images\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"manufacturermaterials\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "document_sections_images", "columns": ["stateid", "menu_id"]}], "writes": [{"table": "manufacturermaterials", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"english_premier_league\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "english_premier_league", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO field5 SELECT co2_emissions, brand_id, starting_year FROM bi.clicks_hourly WHERE co2_emissions > 450\"\n", "labels": {"reads": [{"table": "bi.clicks_hourly", "columns": ["co2_emissions", "brand_id", "starting_year"]}], "writes": [{"table": "field5", "columns": ["co2_emissions", "brand_id", "starting_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO emerging_markets.digital_assets SELECT lesson_status_code, prod_date, policyholder_id, max_speed FROM public.forest_stats WHERE lesson_status_code > 161\"\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": ["lesson_status_code", "prod_date", "policyholder_id", "max_speed"]}], "writes": [{"table": "emerging_markets.digital_assets", "columns": ["lesson_status_code", "prod_date", "policyholder_id", "max_speed"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT author_id, total_spent FROM humanitarianmissions\", engine)\nimport logging\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"languages\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "humanitarianmissions", "columns": ["author_id", "total_spent"]}], "writes": [{"table": "languages", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods_payments_delta SELECT typical_buying_price, commission_pct FROM revenue WHERE typical_buying_price > 218\"\n", "labels": {"reads": [{"table": "revenue", "columns": ["typical_buying_price", "commission_pct"]}], "writes": [{"table": "ods_payments_delta", "columns": ["typical_buying_price", "commission_pct"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"swimmer\")\nsrc.write.insertInto(\"workforce_development\", overwrite=True)\n", "labels": {"reads": [{"table": "swimmer", "columns": null}], "writes": [{"table": "workforce_development", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT long, num_virtual_tours FROM menu_categories LIMIT 112\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "menu_categories", "columns": ["long", "num_virtual_tours"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO ads.ads_products_hourly SELECT operation_name, allocation_date FROM mineral_extraction_us WHERE operation_name > 317\");\n", "labels": {"reads": [{"table": "mineral_extraction_us", "columns": ["operation_name", "allocation_date"]}], "writes": [{"table": "ads.ads_products_hourly", "columns": ["operation_name", "allocation_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"platform\")\nsrc.write.insertInto(\"healthequitymetrics\", overwrite=True)\n", "labels": {"reads": [{"table": "platform", "columns": null}], "writes": [{"table": "healthequitymetrics", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"customersregion\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "customersregion", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO dp_articles SELECT acc_percent, num_of_stock, museum, chip_model FROM displaced_people WHERE acc_percent > 236\")\n", "labels": {"reads": [{"table": "displaced_people", "columns": ["acc_percent", "num_of_stock", "museum", "chip_model"]}], "writes": [{"table": "dp_articles", "columns": ["acc_percent", "num_of_stock", "museum", "chip_model"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mining_operations\").where(\"dt = current_date()\").writeTo(\"party_events\").append()\n", "labels": {"reads": [{"table": "mining_operations", "columns": null}], "writes": [{"table": "party_events", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"astronaut_missions\")\npush_to_target(df, \"infantmortalitydata\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": null}], "writes": [{"table": "infantmortalitydata", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw.dw_coupon_use_daily\")\nsrc.write.insertInto(\"mart.clicks\", overwrite=True)\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": null}], "writes": [{"table": "mart.clicks", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"video_games\")\nsrc.write.insertInto(\"safe_dataset\", overwrite=True)\n", "labels": {"reads": [{"table": "video_games", "columns": null}], "writes": [{"table": "safe_dataset", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ota_revenue\");\ndf.write().mode(\"overwrite\").saveAsTable(\"sustainable_building\");\n", "labels": {"reads": [{"table": "ota_revenue", "columns": null}], "writes": [{"table": "sustainable_building", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fair_wages\").toPandas()\ndf[[\"policy_description\", \"stream_date\"]].to_sql(\"vessels\", engine, index=False)\n", "labels": {"reads": [{"table": "fair_wages", "columns": null}], "writes": [{"table": "vessels", "columns": ["policy_description", "stream_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO agricultural_innovation_projects SELECT date_joined_staff, area_sqkm FROM containers WHERE date_joined_staff > 29\")\n", "labels": {"reads": [{"table": "containers", "columns": ["date_joined_staff", "area_sqkm"]}], "writes": [{"table": "agricultural_innovation_projects", "columns": ["date_joined_staff", "area_sqkm"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table vehiclemodels --columns productname,is_valid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "vehiclemodels", "columns": ["productname", "is_valid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO donors_region SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO tech_workers_union SELECT num_accessible_tech_centers, emp_id FROM emergency_categories WHERE num_accessible_tech_centers > 20\"\n", "labels": {"reads": [{"table": "emergency_categories", "columns": ["num_accessible_tech_centers", "emp_id"]}], "writes": [{"table": "tech_workers_union", "columns": ["num_accessible_tech_centers", "emp_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM green_certification\"\n", "labels": {"reads": [{"table": "green_certification", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO browser SELECT communitytype, characteristic_name, initiative FROM projecttimelinebybudget WHERE communitytype > 205\"], check=True)\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": ["communitytype", "characteristic_name", "initiative"]}], "writes": [{"table": "browser", "columns": ["communitytype", "characteristic_name", "initiative"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws.dws_clicks_full SELECT * FROM legacy\ncur.execute(\"SELECT request_id, subject_id FROM building_permits LIMIT 128\")\n", "labels": {"reads": [{"table": "building_permits", "columns": ["request_id", "subject_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"pacific_ocean\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"life_expectancy\")\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": null}], "writes": [{"table": "life_expectancy", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"route_fares\")\nsrc.write.insertInto(\"bi.inventory_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "route_fares", "columns": null}], "writes": [{"table": "bi.inventory_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 60;\nSQL\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": ["vendor_state", "wage_increase"]}, {"table": "regional_archaeologists", "columns": ["staff_name", "start_date", "safety_record"]}], "writes": [{"table": "agriculturalinnovations", "columns": ["staff_name", "start_date", "safety_record"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO crops_year SELECT color_code, branch, billingcountry, crs_code FROM dw.dw_users_di WHERE color_code > 217\"], check=True)\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": ["color_code", "branch", "billingcountry", "crs_code"]}], "writes": [{"table": "crops_year", "columns": ["color_code", "branch", "billingcountry", "crs_code"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table disabilityadvocacy --columns facility_name,recruitername --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "disabilityadvocacy", "columns": ["facility_name", "recruitername"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"minor_in\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"rent_arrears\")\n", "labels": {"reads": [{"table": "minor_in", "columns": null}], "writes": [{"table": "rent_arrears", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO doctors (lieutenant_governor, apid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "doctors", "columns": ["lieutenant_governor", "apid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model gamesales depends on mart.inventory_hourly\ndbt run -s gamesales --vars '{\"source_table\":\"mart.inventory_hourly\"}'\n", "labels": {"reads": [{"table": "mart.inventory_hourly", "columns": null}], "writes": [{"table": "gamesales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ods.ods_member_point_delta\").where(\"dt = current_date()\").writeTo(\"spacecraft\").append()\n", "labels": {"reads": [{"table": "ods.ods_member_point_delta", "columns": null}], "writes": [{"table": "spacecraft", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO contract_negotiations (architect_id, team_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "contract_negotiations", "columns": ["architect_id", "team_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 138;\nEOF\n", "labels": {"reads": [{"table": "voyages", "columns": ["storename", "clicks", "cruelty_free"]}], "writes": [{"table": "surveylocations", "columns": ["storename", "clicks", "cruelty_free"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rehab_centers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"manufacturingplants\")\n", "labels": {"reads": [{"table": "rehab_centers", "columns": null}], "writes": [{"table": "manufacturingplants", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"infection_rates\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"files\")\n", "labels": {"reads": [{"table": "infection_rates", "columns": null}], "writes": [{"table": "files", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg.refunds (openingid, vessel_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg.refunds", "columns": ["openingid", "vessel_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_temperatures\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"camera_lens\")\n", "labels": {"reads": [{"table": "ocean_temperatures", "columns": null}], "writes": [{"table": "camera_lens", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO scientists SELECT transaction_value, payment_method, delivery_date, center FROM dwd.inventory_df WHERE transaction_value > 165\"\n", "labels": {"reads": [{"table": "dwd.inventory_df", "columns": ["transaction_value", "payment_method", "delivery_date", "center"]}], "writes": [{"table": "scientists", "columns": ["transaction_value", "payment_method", "delivery_date", "center"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stg.stg_inventory_full\");\ndf.write().mode(\"overwrite\").saveAsTable(\"workercontactinfo\");\n", "labels": {"reads": [{"table": "stg.stg_inventory_full", "columns": null}], "writes": [{"table": "workercontactinfo", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"platformg\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "platformg", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"timed_status_of_things\")\nsrc.write.insertInto(\"sourcing\", overwrite=True)\n", "labels": {"reads": [{"table": "timed_status_of_things", "columns": null}], "writes": [{"table": "sourcing", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_cart_item_hourly SELECT * FROM legacy\ncur.execute(\"SELECT shelter_name, campaign_name FROM instructor LIMIT 422\")\n", "labels": {"reads": [{"table": "instructor", "columns": ["shelter_name", "campaign_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"workers\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "workers", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO programs SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT framework, donor_program FROM video_content\", engine)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"mart.vendors_full\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "video_content", "columns": ["framework", "donor_program"]}], "writes": [{"table": "mart.vendors_full", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dw.dw_events_di\", conn)\ndf.to_sql(\"green_buildings_us\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dw.dw_events_di", "columns": null}], "writes": [{"table": "green_buildings_us", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO taj_mahal_visitors SELECT flag, therapy_session, hire_date FROM displaced_people WHERE flag > 224\"\n", "labels": {"reads": [{"table": "displaced_people", "columns": ["flag", "therapy_session", "hire_date"]}], "writes": [{"table": "taj_mahal_visitors", "columns": ["flag", "therapy_session", "hire_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"sustainable_sourcing\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "sustainable_sourcing", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table inst --columns source,album_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "inst", "columns": ["source", "album_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO commodity_prices SELECT commodity, total_passengers, courses, waste_generation FROM vesselfuel WHERE commodity > 366\")\n", "labels": {"reads": [{"table": "vesselfuel", "columns": ["commodity", "total_passengers", "courses", "waste_generation"]}], "writes": [{"table": "commodity_prices", "columns": ["commodity", "total_passengers", "courses", "waste_generation"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO world_heritage_sites SELECT startup_id, half FROM discipline_enrollments WHERE startup_id > 237\");\n", "labels": {"reads": [{"table": "discipline_enrollments", "columns": ["startup_id", "half"]}], "writes": [{"table": "world_heritage_sites", "columns": ["startup_id", "half"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"station_company\")\npush_to_output(df, \"ads.ads_cart_item_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "station_company", "columns": null}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"ods_cart_item_df\")\npersist_to_target(df, \"suburbs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods_cart_item_df", "columns": null}], "writes": [{"table": "suburbs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO researcher SELECT events, login_name, sales_details FROM facility WHERE events > 108\")\n", "labels": {"reads": [{"table": "facility", "columns": ["events", "login_name", "sales_details"]}], "writes": [{"table": "researcher", "columns": ["events", "login_name", "sales_details"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"tv_shows_genre\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "tv_shows_genre", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT property_price, cases_handled FROM recyclingratessouthamerica LIMIT 351\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "recyclingratessouthamerica", "columns": ["property_price", "cases_handled"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO fare_collection SELECT workoutdate, post_date, restaurant_name, taskid FROM artcollection WHERE workoutdate > 214\"\n", "labels": {"reads": [{"table": "artcollection", "columns": ["workoutdate", "post_date", "restaurant_name", "taskid"]}], "writes": [{"table": "fare_collection", "columns": ["workoutdate", "post_date", "restaurant_name", "taskid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table marketingbudget --columns labor_hour_id,intervention_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "marketingbudget", "columns": ["labor_hour_id", "intervention_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"security_incidents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "security_incidents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO spacecraft SELECT competition, staff_gender, professional_development FROM spacecrafts WHERE competition > 156\")\n", "labels": {"reads": [{"table": "spacecrafts", "columns": ["competition", "staff_gender", "professional_development"]}], "writes": [{"table": "spacecraft", "columns": ["competition", "staff_gender", "professional_development"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO districts SELECT * FROM legacy\nspark.sql(\"INSERT INTO mart.mart_shipments_hourly SELECT age_group_id, end_station_id, vol_id, billingcountry FROM perpetrator WHERE age_group_id > 400\")\n", "labels": {"reads": [{"table": "perpetrator", "columns": ["age_group_id", "end_station_id", "vol_id", "billingcountry"]}], "writes": [{"table": "mart.mart_shipments_hourly", "columns": ["age_group_id", "end_station_id", "vol_id", "billingcountry"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT tech, profession FROM galleryc LIMIT 382\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "galleryc", "columns": ["tech", "profession"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT family_name, framework_id FROM researchgrants LIMIT 119\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO socialimpactinvestments SELECT provider_name, asset_type FROM institution WHERE provider_name > 258\")\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["family_name", "framework_id"]}, {"table": "institution", "columns": ["provider_name", "asset_type"]}], "writes": [{"table": "socialimpactinvestments", "columns": ["provider_name", "asset_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 97;\nSQL\n", "labels": {"reads": [{"table": "budget_allocations", "columns": ["pollutant_type", "tourist_id"]}, {"table": "attorneylocationyear", "columns": ["saleid", "longitude", "report_id", "staff_details"]}], "writes": [{"table": "pollutionincidents", "columns": ["saleid", "longitude", "report_id", "staff_details"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT claim_header_id, award FROM france_culture\", engine)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"sustainable_materials\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "france_culture", "columns": ["claim_header_id", "award"]}], "writes": [{"table": "sustainable_materials", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"cuisine\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"crop_temperature\")\n", "labels": {"reads": [{"table": "cuisine", "columns": null}], "writes": [{"table": "crop_temperature", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO athlete_stats SELECT no_of_customers, budget_amount FROM solar_farms WHERE no_of_customers > 492\"\n", "labels": {"reads": [{"table": "solar_farms", "columns": ["no_of_customers", "budget_amount"]}], "writes": [{"table": "athlete_stats", "columns": ["no_of_customers", "budget_amount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO dws.coupon_use_di SELECT a.startdate, b.topic FROM user_profiles a JOIN threat_intelligence b ON a.genrename = b.genrename\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "user_profiles", "columns": null}, {"table": "threat_intelligence", "columns": null}], "writes": [{"table": "dws.coupon_use_di", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT bookings, bats FROM dws_coupon_use\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ndf.to_sql(\"dw.clicks_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["bookings", "bats"]}], "writes": [{"table": "dw.clicks_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"market_access\").where(\"dt = current_date()\").writeTo(\"community.donors\").append()\n", "labels": {"reads": [{"table": "market_access", "columns": null}], "writes": [{"table": "community.donors", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO show SELECT * FROM legacy\nspark.sql(\"INSERT INTO marine_species_status SELECT date_of_publication, festival_name FROM ods.ods_events_daily WHERE date_of_publication > 379\")\n", "labels": {"reads": [{"table": "ods.ods_events_daily", "columns": ["date_of_publication", "festival_name"]}], "writes": [{"table": "marine_species_status", "columns": ["date_of_publication", "festival_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table volunteer_signups --columns taskdate,contextid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "volunteer_signups", "columns": ["taskdate", "contextid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO protein (status_code, water_temp) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "protein", "columns": ["status_code", "water_temp"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"wind_energy_projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"landfill_capacity_north_america\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "landfill_capacity_north_america", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model fleet depends on ai_projects\ndbt run --models fleet --vars '{\"source_table\":\"ai_projects\"}'\n", "labels": {"reads": [{"table": "ai_projects", "columns": null}], "writes": [{"table": "fleet", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM weapons\"\n", "labels": {"reads": [{"table": "weapons", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM savings\"\n", "labels": {"reads": [{"table": "savings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"rent_arrears\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "rent_arrears", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO policy SELECT * FROM legacy\nspark.sql(\"INSERT INTO threat_intel SELECT reviews, available_yn, mar FROM domesticconferences WHERE reviews > 19\")\n", "labels": {"reads": [{"table": "domesticconferences", "columns": ["reviews", "available_yn", "mar"]}], "writes": [{"table": "threat_intel", "columns": ["reviews", "available_yn", "mar"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table game_results --target-dir /tmp/land\n", "labels": {"reads": [{"table": "game_results", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM menu_vendors\", conn)\ndf.to_sql(\"militarypersonnel\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "menu_vendors", "columns": null}], "writes": [{"table": "militarypersonnel", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT founding_date, votes FROM hall_of_fame LIMIT 288\")\nresult = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO injury_accident SELECT port, years_operating, economic_impact, bikes_available FROM movie_financials WHERE port > 322\")\n", "labels": {"reads": [{"table": "hall_of_fame", "columns": ["founding_date", "votes"]}, {"table": "movie_financials", "columns": ["port", "years_operating", "economic_impact", "bikes_available"]}], "writes": [{"table": "injury_accident", "columns": ["port", "years_operating", "economic_impact", "bikes_available"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT cloud_cover, posts_per_day FROM veteran_occupations\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nimport logging\ndf.to_sql(\"minor_in\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "veteran_occupations", "columns": ["cloud_cover", "posts_per_day"]}], "writes": [{"table": "minor_in", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT heritage_site_id, recycler_id FROM mediators LIMIT 246\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "mediators", "columns": ["heritage_site_id", "recycler_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 258;\nEOF\n", "labels": {"reads": [{"table": "mountain", "columns": ["author_community", "month", "cultural_significance", "rows"]}], "writes": [{"table": "mines", "columns": ["author_community", "month", "cultural_significance", "rows"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"waste_data\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.bi_payments_full\")\n", "labels": {"reads": [{"table": "waste_data", "columns": null}], "writes": [{"table": "bi.bi_payments_full", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"athletes\")\nsrc.write.insertInto(\"financial_transactions\", overwrite=True)\n", "labels": {"reads": [{"table": "athletes", "columns": null}], "writes": [{"table": "financial_transactions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model tv_shows depends on noise_pollution\ndbt run -s tv_shows --vars 'source: noise_pollution'\n", "labels": {"reads": [{"table": "noise_pollution", "columns": null}], "writes": [{"table": "tv_shows", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 388;\nEOF\n", "labels": {"reads": [{"table": "class", "columns": ["user_login", "menuitem"]}], "writes": [{"table": "highest_scores", "columns": ["user_login", "menuitem"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"endowment\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"smartcities\")\n", "labels": {"reads": [{"table": "endowment", "columns": null}], "writes": [{"table": "smartcities", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"maintenance_requests\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO productivity SELECT oct, has_spf, shares, school FROM india_ingredient_sourcing WHERE oct > 156\"\n", "labels": {"reads": [{"table": "india_ingredient_sourcing", "columns": ["oct", "has_spf", "shares", "school"]}], "writes": [{"table": "productivity", "columns": ["oct", "has_spf", "shares", "school"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"genetics.projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"support\")\n", "labels": {"reads": [{"table": "genetics.projects", "columns": null}], "writes": [{"table": "support", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ocean_floor_mapping SELECT * FROM legacy\nspark.sql(\"INSERT INTO defense_spending_3 SELECT course_completion, refugee_name FROM donations_insert_2 WHERE course_completion > 489\")\n", "labels": {"reads": [{"table": "donations_insert_2", "columns": ["course_completion", "refugee_name"]}], "writes": [{"table": "defense_spending_3", "columns": ["course_completion", "refugee_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model clicks_delta depends on labor_hours\ndbt run --models clicks_delta --vars '{\"source_table\":\"labor_hours\"}'\n", "labels": {"reads": [{"table": "labor_hours", "columns": null}], "writes": [{"table": "clicks_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.stg_users_di SELECT menuitemid, issues FROM factories_africa WHERE menuitemid > 11\"], check=True)\n", "labels": {"reads": [{"table": "factories_africa", "columns": ["menuitemid", "issues"]}], "writes": [{"table": "stg.stg_users_di", "columns": ["menuitemid", "issues"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table electronics_factories --target-dir /tmp/land\n", "labels": {"reads": [{"table": "electronics_factories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO browser SELECT funding_id, copy_number FROM volunteerhours WHERE funding_id > 99\"\n", "labels": {"reads": [{"table": "volunteerhours", "columns": ["funding_id", "copy_number"]}], "writes": [{"table": "browser", "columns": ["funding_id", "copy_number"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tryout\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"retailers\")\n", "labels": {"reads": [{"table": "tryout", "columns": null}], "writes": [{"table": "retailers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO check_ins SELECT commission_pct, practices, date_incident_end FROM customer_master_index WHERE commission_pct > 498\");\n", "labels": {"reads": [{"table": "customer_master_index", "columns": ["commission_pct", "practices", "date_incident_end"]}], "writes": [{"table": "check_ins", "columns": ["commission_pct", "practices", "date_incident_end"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO inspections SELECT detention_type_description, ad_id, shipped_to FROM dws.events WHERE detention_type_description > 340\"], check=True)\n", "labels": {"reads": [{"table": "dws.events", "columns": ["detention_type_description", "ad_id", "shipped_to"]}], "writes": [{"table": "inspections", "columns": ["detention_type_description", "ad_id", "shipped_to"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"races\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "races", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi.bi_risk_score_full SELECT screening_id, incident_count, donor_id FROM shoes WHERE screening_id > 280\"\n", "labels": {"reads": [{"table": "shoes", "columns": ["screening_id", "incident_count", "donor_id"]}], "writes": [{"table": "bi.bi_risk_score_full", "columns": ["screening_id", "incident_count", "donor_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO astronautmedicaldata SELECT a.middle_name, b.ota_id FROM ods_risk_score_delta a JOIN mart.mart_device_log b ON a.offender_name = b.offender_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods_risk_score_delta", "columns": null}, {"table": "mart.mart_device_log", "columns": null}], "writes": [{"table": "astronautmedicaldata", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 269;\nSQL\n", "labels": {"reads": [{"table": "indian_ocean_fishingvessels", "columns": ["item_price", "prof_num"]}, {"table": "deliveryaddresses", "columns": ["how_to_get_there", "donor_id", "funding", "playerregion"]}], "writes": [{"table": "player_award", "columns": ["how_to_get_there", "donor_id", "funding", "playerregion"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"open_pedagogy\").toPandas()\ndf[[\"contractorname\", \"foreign\"]].to_sql(\"infra_diversification\", engine, index=False)\n", "labels": {"reads": [{"table": "open_pedagogy", "columns": null}], "writes": [{"table": "infra_diversification", "columns": ["contractorname", "foreign"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO member_activity SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model green_certification depends on chip_model\ndbt run -s green_certification --vars '{\"src\":\"chip_model\"}'\n", "labels": {"reads": [{"table": "chip_model", "columns": null}], "writes": [{"table": "green_certification", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods.ods_payments_full SELECT attack_count, inclusive, capacity FROM broadband_subscribers WHERE attack_count > 190\"\n", "labels": {"reads": [{"table": "broadband_subscribers", "columns": ["attack_count", "inclusive", "capacity"]}], "writes": [{"table": "ods.ods_payments_full", "columns": ["attack_count", "inclusive", "capacity"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO whale_sightings SELECT garmentid, delivery_id, claimtype, offense FROM visitor_exhibition WHERE garmentid > 378\")\n", "labels": {"reads": [{"table": "visitor_exhibition", "columns": ["garmentid", "delivery_id", "claimtype", "offense"]}], "writes": [{"table": "whale_sightings", "columns": ["garmentid", "delivery_id", "claimtype", "offense"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"excavations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stock_levels\")\n", "labels": {"reads": [{"table": "excavations", "columns": null}], "writes": [{"table": "stock_levels", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"recyclingrates\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "recyclingrates", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO operation SELECT organization, competition FROM communitycourtcases WHERE organization > 483\"], check=True)\n", "labels": {"reads": [{"table": "communitycourtcases", "columns": ["organization", "competition"]}], "writes": [{"table": "operation", "columns": ["organization", "competition"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT emissions, reader_id FROM urban_transportation LIMIT 453\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "urban_transportation", "columns": ["emissions", "reader_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM consumer_preference\"\n", "labels": {"reads": [{"table": "consumer_preference", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO tokyo_water_consumption SELECT union_name, trend FROM startup_founders WHERE union_name > 302\"\n", "labels": {"reads": [{"table": "startup_founders", "columns": ["union_name", "trend"]}], "writes": [{"table": "tokyo_water_consumption", "columns": ["union_name", "trend"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 169;\nSQL\n", "labels": {"reads": [{"table": "gymc_members", "columns": ["workout_type", "consider_rate"]}, {"table": "stg.coupon_use", "columns": ["contributorid", "birth_date", "news_outlet", "units_owned"]}], "writes": [{"table": "bioprocesses", "columns": ["contributorid", "birth_date", "news_outlet", "units_owned"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO workercontactinfo SELECT department, investor_name FROM culturalpractices WHERE department > 406\")\n", "labels": {"reads": [{"table": "culturalpractices", "columns": ["department", "investor_name"]}], "writes": [{"table": "workercontactinfo", "columns": ["department", "investor_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model bike_station_info depends on trends_2022\ndbt build -s bike_station_info --vars 'source: trends_2022'\n", "labels": {"reads": [{"table": "trends_2022", "columns": null}], "writes": [{"table": "bike_station_info", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"asset_parts\")\nsrc.write.insertInto(\"defense_personnel\", overwrite=True)\n", "labels": {"reads": [{"table": "asset_parts", "columns": null}], "writes": [{"table": "defense_personnel", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table projects --columns review_text,approach --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "projects", "columns": ["review_text", "approach"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO subjects SELECT schedule_id, is_vegetarian, parameters FROM strategies WHERE schedule_id > 242\"], check=True)\n", "labels": {"reads": [{"table": "strategies", "columns": ["schedule_id", "is_vegetarian", "parameters"]}], "writes": [{"table": "subjects", "columns": ["schedule_id", "is_vegetarian", "parameters"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO mart.campaigns_di SELECT dish_id, rate FROM dishes WHERE dish_id > 306\");\n", "labels": {"reads": [{"table": "dishes", "columns": ["dish_id", "rate"]}], "writes": [{"table": "mart.campaigns_di", "columns": ["dish_id", "rate"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO virtual_tour_revenue SELECT mhw_id, sector FROM geological_survey WHERE mhw_id > 412\"\n", "labels": {"reads": [{"table": "geological_survey", "columns": ["mhw_id", "sector"]}], "writes": [{"table": "virtual_tour_revenue", "columns": ["mhw_id", "sector"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"beverages\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "beverages", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO sustainable_urban_properties_2 SELECT sales_id, number_city_affected FROM materials WHERE sales_id > 6\");\n", "labels": {"reads": [{"table": "materials", "columns": ["sales_id", "number_city_affected"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["sales_id", "number_city_affected"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO station_company SELECT premise_id, skill_description, trial_name, assistingnurse FROM bay_area_properties WHERE premise_id > 161\");\n", "labels": {"reads": [{"table": "bay_area_properties", "columns": ["premise_id", "skill_description", "trial_name", "assistingnurse"]}], "writes": [{"table": "station_company", "columns": ["premise_id", "skill_description", "trial_name", "assistingnurse"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO iron (lipstick_id, payment_method) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "iron", "columns": ["lipstick_id", "payment_method"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT fastestlapspeed, workshop_name FROM educationprograms LIMIT 487\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "educationprograms", "columns": ["fastestlapspeed", "workshop_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"airportdata\").toPandas()\ndf[[\"grant_amount\", \"report_date\"]].to_sql(\"mart.campaigns_full\", engine, index=False)\n", "labels": {"reads": [{"table": "airportdata", "columns": null}], "writes": [{"table": "mart.campaigns_full", "columns": ["grant_amount", "report_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO coralreefs SELECT * FROM legacy\ncur.execute(\"SELECT discount, race FROM auto_shows LIMIT 417\")\n", "labels": {"reads": [{"table": "auto_shows", "columns": ["discount", "race"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model decentralized_apps depends on orgdonations\ndbt build --select decentralized_apps --vars '{\"src\":\"orgdonations\"}'\n", "labels": {"reads": [{"table": "orgdonations", "columns": null}], "writes": [{"table": "decentralized_apps", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 499;\nSQL\n", "labels": {"reads": [{"table": "territory.human_rights_data", "columns": ["athlete", "operationdate"]}, {"table": "public.crime_types", "columns": ["chemical_type", "industry"]}], "writes": [{"table": "southeast_providers", "columns": ["chemical_type", "industry"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO mart.clicks_delta SELECT performance_id, mean_temperature_f, sessiondate FROM regional_railways WHERE performance_id > 37\"\n", "labels": {"reads": [{"table": "regional_railways", "columns": ["performance_id", "mean_temperature_f", "sessiondate"]}], "writes": [{"table": "mart.clicks_delta", "columns": ["performance_id", "mean_temperature_f", "sessiondate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tourismproviders\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"gender\")\n", "labels": {"reads": [{"table": "tourismproviders", "columns": null}], "writes": [{"table": "gender", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ads.ads_users_hourly SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vessel_registry SELECT a.attraction_type_description, b.drought_id FROM prices a JOIN cultivators b ON a.gamepreference = b.gamepreference\"\n", "labels": {"reads": [{"table": "prices", "columns": null}, {"table": "cultivators", "columns": null}], "writes": [{"table": "vessel_registry", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO hotel_reviews SELECT * FROM legacy\nspark.sql(\"INSERT INTO ocean_floor_mapping SELECT recycler_id, report_date, cid, crs_code FROM vessel_incident_count WHERE recycler_id > 350\")\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": ["recycler_id", "report_date", "cid", "crs_code"]}], "writes": [{"table": "ocean_floor_mapping", "columns": ["recycler_id", "report_date", "cid", "crs_code"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"zip_codes\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"player_sessions\")\n", "labels": {"reads": [{"table": "zip_codes", "columns": null}], "writes": [{"table": "player_sessions", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 126;\nEOF\n", "labels": {"reads": [{"table": "fabrics", "columns": ["valuation", "route_id", "therapy_date", "numhearings"]}], "writes": [{"table": "on_call", "columns": ["valuation", "route_id", "therapy_date", "numhearings"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model gamereviews depends on wastewater_plants\ndbt build -s gamereviews --vars '{\"source_table\":\"wastewater_plants\"}'\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": null}], "writes": [{"table": "gamereviews", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO police_emergencies SELECT farm_id, founded_year FROM city_department WHERE farm_id > 268\")\n", "labels": {"reads": [{"table": "city_department", "columns": ["farm_id", "founded_year"]}], "writes": [{"table": "police_emergencies", "columns": ["farm_id", "founded_year"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO entrepreneur SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO drugs SELECT grant_id, day_of_week, policyid FROM dwd_sessions_hourly WHERE grant_id > 69\"], check=True)\n", "labels": {"reads": [{"table": "dwd_sessions_hourly", "columns": ["grant_id", "day_of_week", "policyid"]}], "writes": [{"table": "drugs", "columns": ["grant_id", "day_of_week", "policyid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"traditional_arts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"donationsbycause\")\n", "labels": {"reads": [{"table": "traditional_arts", "columns": null}], "writes": [{"table": "donationsbycause", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 312;\nEOF\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": ["num_pallets", "analysis_date"]}], "writes": [{"table": "event", "columns": ["num_pallets", "analysis_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO social_good_projects SELECT iata, cruelty_free FROM fraud_detections WHERE iata > 118\")\n", "labels": {"reads": [{"table": "fraud_detections", "columns": ["iata", "cruelty_free"]}], "writes": [{"table": "social_good_projects", "columns": ["iata", "cruelty_free"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.manufacturerid > 485).all()\n# src table: workouts\nengine.execute(\"INSERT INTO bi.coupon_use SELECT * FROM workouts\")\n", "labels": {"reads": [{"table": "workouts", "columns": null}], "writes": [{"table": "bi.coupon_use", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT menuitemid, sale_revenue FROM safetytestingcounts\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"marine_life_data\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "safetytestingcounts", "columns": ["menuitemid", "sale_revenue"]}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dw.inventory_delta\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO material_production SELECT fan_name, daily_co2_emission, customer_address, score FROM species_forests WHERE fan_name > 415\")\n", "labels": {"reads": [{"table": "species_forests", "columns": ["fan_name", "daily_co2_emission", "customer_address", "score"]}], "writes": [{"table": "material_production", "columns": ["fan_name", "daily_co2_emission", "customer_address", "score"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM e_scooter_trips\"\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart.mart_products_hourly\"\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"match_result\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"voting_record\")\n", "labels": {"reads": [{"table": "match_result", "columns": null}], "writes": [{"table": "voting_record", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 330;\nSQL\n", "labels": {"reads": [{"table": "watertreatmentplants", "columns": ["statename", "funding"]}, {"table": "well_production", "columns": ["individual_name", "played", "result"]}], "writes": [{"table": "editor", "columns": ["individual_name", "played", "result"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT complaint_status_code, artist FROM conservation_programs\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"police_stations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "conservation_programs", "columns": ["complaint_status_code", "artist"]}], "writes": [{"table": "police_stations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT account_number, safety_record FROM ods.ods_campaigns_delta LIMIT 91\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": ["account_number", "safety_record"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO ods.ods_coupon_use_di SELECT media_type_id, area_sqkm, shipping_mode FROM gardens WHERE media_type_id > 14\")\n", "labels": {"reads": [{"table": "gardens", "columns": ["media_type_id", "area_sqkm", "shipping_mode"]}], "writes": [{"table": "ods.ods_coupon_use_di", "columns": ["media_type_id", "area_sqkm", "shipping_mode"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO construction_union SELECT a.vesselid, b.date_in_location_from FROM menu_categories a JOIN volume b ON a.building_short_name = b.building_short_name\"\n", "labels": {"reads": [{"table": "menu_categories", "columns": null}, {"table": "volume", "columns": null}], "writes": [{"table": "construction_union", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO autoshow (certification_id, num_accessible_tech_centers) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "autoshow", "columns": ["certification_id", "num_accessible_tech_centers"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO products_in_events SELECT section_title, card_type_code, calendar_date FROM marine_species_status WHERE section_title > 283\"\n", "labels": {"reads": [{"table": "marine_species_status", "columns": ["section_title", "card_type_code", "calendar_date"]}], "writes": [{"table": "products_in_events", "columns": ["section_title", "card_type_code", "calendar_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT end_time, supplier_id FROM wastegeneration\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"space_missions_2\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "wastegeneration", "columns": ["end_time", "supplier_id"]}], "writes": [{"table": "space_missions_2", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO vessel_registry SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT assessmentdate, actual_order_id FROM bi_refunds_daily LIMIT 492\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO sustainability_initiatives SELECT statename, nominee, service_name, coupon_id FROM dorm_amenity WHERE statename > 264\")\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["assessmentdate", "actual_order_id"]}, {"table": "dorm_amenity", "columns": ["statename", "nominee", "service_name", "coupon_id"]}], "writes": [{"table": "sustainability_initiatives", "columns": ["statename", "nominee", "service_name", "coupon_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO organic_farms SELECT 1\"\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"investor\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"feed\")\n", "labels": {"reads": [{"table": "investor", "columns": null}], "writes": [{"table": "feed", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO bioprocess_engineering SELECT authorder, trial_status, aircraft_id FROM mart.mart_shipments_hourly WHERE authorder > 60\")\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": ["authorder", "trial_status", "aircraft_id"]}], "writes": [{"table": "bioprocess_engineering", "columns": ["authorder", "trial_status", "aircraft_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table satellitedata --target-dir /tmp/land\n", "labels": {"reads": [{"table": "satellitedata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT incident_type_code, quantity_containers FROM bi.bi_events_daily LIMIT 171\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO producers SELECT all_home, mediatorid, stocking_density FROM hotel_tech_adoptions WHERE all_home > 122\")\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": ["incident_type_code", "quantity_containers"]}, {"table": "hotel_tech_adoptions", "columns": ["all_home", "mediatorid", "stocking_density"]}], "writes": [{"table": "producers", "columns": ["all_home", "mediatorid", "stocking_density"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dispensary_sales SELECT judge_id, range, effort_id FROM taxi_data WHERE judge_id > 12\"], check=True)\n", "labels": {"reads": [{"table": "taxi_data", "columns": ["judge_id", "range", "effort_id"]}], "writes": [{"table": "dispensary_sales", "columns": ["judge_id", "range", "effort_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"expensive_space_missions\").where(\"dt = current_date()\").writeTo(\"drug_approvals\").append()\n", "labels": {"reads": [{"table": "expensive_space_missions", "columns": null}], "writes": [{"table": "drug_approvals", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"clothingitems\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "clothingitems", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO concert_events SELECT job, projectid, org_id, group_id FROM dw.shipments_df WHERE job > 368\"\n", "labels": {"reads": [{"table": "dw.shipments_df", "columns": ["job", "projectid", "org_id", "group_id"]}], "writes": [{"table": "concert_events", "columns": ["job", "projectid", "org_id", "group_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"locations_oceania\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stateinfrastructure\")\n", "labels": {"reads": [{"table": "locations_oceania", "columns": null}], "writes": [{"table": "stateinfrastructure", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"wind_energy\").where(\"dt = current_date()\").writeTo(\"bridge\").append()\n", "labels": {"reads": [{"table": "wind_energy", "columns": null}], "writes": [{"table": "bridge", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"tencel_sources\")\nsrc.write.insertInto(\"performances\", overwrite=True)\n", "labels": {"reads": [{"table": "tencel_sources", "columns": null}], "writes": [{"table": "performances", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT activity_name, volunteerid FROM dwd.dwd_cart_item_di LIMIT 435\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": ["activity_name", "volunteerid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO lenders SELECT contributorname, total_shipped, workouttype FROM properties WHERE contributorname > 32\"], check=True)\n", "labels": {"reads": [{"table": "properties", "columns": ["contributorname", "total_shipped", "workouttype"]}], "writes": [{"table": "lenders", "columns": ["contributorname", "total_shipped", "workouttype"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO document_functional_areas SELECT added_date, job_title, subject, patient_age FROM menu_item WHERE added_date > 329\"\n", "labels": {"reads": [{"table": "menu_item", "columns": ["added_date", "job_title", "subject", "patient_age"]}], "writes": [{"table": "document_functional_areas", "columns": ["added_date", "job_title", "subject", "patient_age"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.value_points > 186).all()\n# src table: manufacturers\nengine.execute(\"INSERT INTO green_building_projects SELECT * FROM manufacturers\")\n", "labels": {"reads": [{"table": "manufacturers", "columns": null}], "writes": [{"table": "green_building_projects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ai_safety_papers2 SELECT char_cells, operation_type, international_passengers, store_name FROM soil_moisture WHERE char_cells > 469\"\n", "labels": {"reads": [{"table": "soil_moisture", "columns": ["char_cells", "operation_type", "international_passengers", "store_name"]}], "writes": [{"table": "ai_safety_papers2", "columns": ["char_cells", "operation_type", "international_passengers", "store_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO county SELECT fare_id, resolution FROM mentalhealthprovider WHERE fare_id > 334\");\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": ["fare_id", "resolution"]}], "writes": [{"table": "county", "columns": ["fare_id", "resolution"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO musical SELECT session_language, donor_state, document_description, affirmative FROM dwd_coupon_use_hourly WHERE session_language > 262\")\n", "labels": {"reads": [{"table": "dwd_coupon_use_hourly", "columns": ["session_language", "donor_state", "document_description", "affirmative"]}], "writes": [{"table": "musical", "columns": ["session_language", "donor_state", "document_description", "affirmative"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO eventparticipation SELECT club_name, contractorid, attorney FROM sustainable_building WHERE club_name > 266\"\n", "labels": {"reads": [{"table": "sustainable_building", "columns": ["club_name", "contractorid", "attorney"]}], "writes": [{"table": "eventparticipation", "columns": ["club_name", "contractorid", "attorney"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table field5 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "field5", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO ods_payments_delta SELECT supplier_company_id, menucategory FROM farmers_india WHERE supplier_company_id > 118\");\n", "labels": {"reads": [{"table": "farmers_india", "columns": ["supplier_company_id", "menucategory"]}], "writes": [{"table": "ods_payments_delta", "columns": ["supplier_company_id", "menucategory"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO disabilitysupportprograms SELECT course_description, farmer_id, cost_id, role_code FROM game_sessions WHERE course_description > 21\")\n", "labels": {"reads": [{"table": "game_sessions", "columns": ["course_description", "farmer_id", "cost_id", "role_code"]}], "writes": [{"table": "disabilitysupportprograms", "columns": ["course_description", "farmer_id", "cost_id", "role_code"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table market --target-dir /tmp/land\n", "labels": {"reads": [{"table": "market", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"trenches\").toPandas()\ndf[[\"dst_apid\", \"song_year\"]].to_sql(\"industry_funding\", engine, index=False)\n", "labels": {"reads": [{"table": "trenches", "columns": null}], "writes": [{"table": "industry_funding", "columns": ["dst_apid", "song_year"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO building SELECT eid, crs_description FROM ods.ods_campaigns_delta WHERE eid > 148\"], check=True)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": ["eid", "crs_description"]}], "writes": [{"table": "building", "columns": ["eid", "crs_description"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fruitimport\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "fruitimport", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 236;\nEOF\n", "labels": {"reads": [{"table": "algorithmic_fairness", "columns": ["sessionid", "vehicle_type"]}], "writes": [{"table": "contract_negotiations", "columns": ["sessionid", "vehicle_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"divisions\")\nexport_to_sink(df, \"ap_budget\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "divisions", "columns": null}], "writes": [{"table": "ap_budget", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM iron_ore_production\"\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"attorneylocationyear\").toPandas()\ndf[[\"professional_development_programs\", \"functional_area_description\"]].to_sql(\"ods_exposure_delta\", engine, index=False)\n", "labels": {"reads": [{"table": "attorneylocationyear", "columns": null}], "writes": [{"table": "ods_exposure_delta", "columns": ["professional_development_programs", "functional_area_description"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO claims_processing_stages SELECT moisture, created_date, home_team FROM device_accessibility WHERE moisture > 208\"\n", "labels": {"reads": [{"table": "device_accessibility", "columns": ["moisture", "created_date", "home_team"]}], "writes": [{"table": "claims_processing_stages", "columns": ["moisture", "created_date", "home_team"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model art depends on movie_financials\ndbt build --select art --vars '{\"source_table\":\"movie_financials\"}'\n", "labels": {"reads": [{"table": "movie_financials", "columns": null}], "writes": [{"table": "art", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO criminal_cases SELECT transaction_id, appelation, bikes_available, launch_id FROM workshops WHERE transaction_id > 45\"\n", "labels": {"reads": [{"table": "workshops", "columns": ["transaction_id", "appelation", "bikes_available", "launch_id"]}], "writes": [{"table": "criminal_cases", "columns": ["transaction_id", "appelation", "bikes_available", "launch_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"exhibition_visitors\").where(\"dt = current_date()\").writeTo(\"dw.clicks_di\").append()\n", "labels": {"reads": [{"table": "exhibition_visitors", "columns": null}], "writes": [{"table": "dw.clicks_di", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO providers SELECT director, date_problem_reported FROM stg_orders_hourly WHERE director > 31\"\n", "labels": {"reads": [{"table": "stg_orders_hourly", "columns": ["director", "date_problem_reported"]}], "writes": [{"table": "providers", "columns": ["director", "date_problem_reported"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO extraction_methods SELECT contact_staff_id, service_name, productlaunchdate FROM dwd.events_daily WHERE contact_staff_id > 325\")\n", "labels": {"reads": [{"table": "dwd.events_daily", "columns": ["contact_staff_id", "service_name", "productlaunchdate"]}], "writes": [{"table": "extraction_methods", "columns": ["contact_staff_id", "service_name", "productlaunchdate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_input(ctx, \"grad_students\")\npersist_to_store(df, \"founders\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "grad_students", "columns": null}], "writes": [{"table": "founders", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO co_ownership_program SELECT gamegenre, bill_id, co_owner_count, contract_end FROM product_details WHERE gamegenre > 287\"], check=True)\n", "labels": {"reads": [{"table": "product_details", "columns": ["gamegenre", "bill_id", "co_owner_count", "contract_end"]}], "writes": [{"table": "co_ownership_program", "columns": ["gamegenre", "bill_id", "co_owner_count", "contract_end"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO support_tickets (card_number, billing_country) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "support_tickets", "columns": ["card_number", "billing_country"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model nba_games depends on bay_area_properties\ndbt run --select nba_games --vars '{\"src\":\"bay_area_properties\"}'\n", "labels": {"reads": [{"table": "bay_area_properties", "columns": null}], "writes": [{"table": "nba_games", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO tokyo_water_consumption SELECT a.staff_details, b.transaction_amount FROM miningoperations a JOIN hospital_visits b ON a.issue_month = b.issue_month\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "miningoperations", "columns": null}, {"table": "hospital_visits", "columns": null}], "writes": [{"table": "tokyo_water_consumption", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO marketing_budgets SELECT a.vehicle_type, b.reader_id FROM project_issues a JOIN miningdepartment b ON a.injured = b.injured\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "project_issues", "columns": null}, {"table": "miningdepartment", "columns": null}], "writes": [{"table": "marketing_budgets", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dw.shipments_di --columns fabrictype,month --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dw.shipments_di", "columns": ["fabrictype", "month"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO cultural_heritage SELECT valuation, route_id FROM obesity WHERE valuation > 10\");\n", "labels": {"reads": [{"table": "obesity", "columns": ["valuation", "route_id"]}], "writes": [{"table": "cultural_heritage", "columns": ["valuation", "route_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO professionals SELECT regulation, safety_id FROM agri_innov WHERE regulation > 87\"], check=True)\n", "labels": {"reads": [{"table": "agri_innov", "columns": ["regulation", "safety_id"]}], "writes": [{"table": "professionals", "columns": ["regulation", "safety_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO emergency_categories SELECT airline, metric_id, observation_date, commanding_officer FROM item_prices WHERE airline > 307\");\n", "labels": {"reads": [{"table": "item_prices", "columns": ["airline", "metric_id", "observation_date", "commanding_officer"]}], "writes": [{"table": "emergency_categories", "columns": ["airline", "metric_id", "observation_date", "commanding_officer"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO residents_services SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"regulatory_frameworks\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 227;\nSQL\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": ["site_id", "is_hybrid"]}, {"table": "coral_reefs", "columns": ["adults", "comment_count", "train_number", "temperature"]}], "writes": [{"table": "construction_labor", "columns": ["adults", "comment_count", "train_number", "temperature"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO faculty_participates_in (start_therapy, mine_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "faculty_participates_in", "columns": ["start_therapy", "mine_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"intelligence_personnel\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artcontributors (healthequitymetricscore, train_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artcontributors", "columns": ["healthequitymetricscore", "train_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO mental_health_parity SELECT claim_date, citizens FROM agro_regions WHERE claim_date > 276\");\n", "labels": {"reads": [{"table": "agro_regions", "columns": ["claim_date", "citizens"]}], "writes": [{"table": "mental_health_parity", "columns": ["claim_date", "citizens"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"festival_detail\")\npersist_to_output(df, \"renewable_power\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "festival_detail", "columns": null}], "writes": [{"table": "renewable_power", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.coupon_use\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"whale_sharks\")\n", "labels": {"reads": [{"table": "ods.coupon_use", "columns": null}], "writes": [{"table": "whale_sharks", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO medical_facilities SELECT num_virtual_tours, completion_date FROM posts WHERE num_virtual_tours > 51\"\n", "labels": {"reads": [{"table": "posts", "columns": ["num_virtual_tours", "completion_date"]}], "writes": [{"table": "medical_facilities", "columns": ["num_virtual_tours", "completion_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO auto_shows SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_likes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"militaryequipmentsales\")\n", "labels": {"reads": [{"table": "user_likes", "columns": null}], "writes": [{"table": "militaryequipmentsales", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT flight_number, initiative FROM dwd.dwd_vendors LIMIT 146\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": ["flight_number", "initiative"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"institution\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ads.orders_daily\");\n", "labels": {"reads": [{"table": "institution", "columns": null}], "writes": [{"table": "ads.orders_daily", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hockey_players\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hockey_players", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM innovation_trends\"\n", "labels": {"reads": [{"table": "innovation_trends", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"missions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.dwd_events_delta\")\n", "labels": {"reads": [{"table": "missions", "columns": null}], "writes": [{"table": "dwd.dwd_events_delta", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO train_lines SELECT gross_worldwide, shipping_mode, survey_id FROM music WHERE gross_worldwide > 102\")\n", "labels": {"reads": [{"table": "music", "columns": ["gross_worldwide", "shipping_mode", "survey_id"]}], "writes": [{"table": "train_lines", "columns": ["gross_worldwide", "shipping_mode", "survey_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hotel_chain_name, wifi FROM supportprograms\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"states\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "supportprograms", "columns": ["hotel_chain_name", "wifi"]}], "writes": [{"table": "states", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"menu_items\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"vendorfabrics\")\n", "labels": {"reads": [{"table": "menu_items", "columns": null}], "writes": [{"table": "vendorfabrics", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"community_members\");\ndf.write().mode(\"overwrite\").saveAsTable(\"shops\");\n", "labels": {"reads": [{"table": "community_members", "columns": null}], "writes": [{"table": "shops", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT saleamount, end_date FROM circular_economy_companies LIMIT 263\")\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO defensespending SELECT organization_name, foreign, head FROM ods.ods_campaigns_df WHERE organization_name > 253\")\n", "labels": {"reads": [{"table": "circular_economy_companies", "columns": ["saleamount", "end_date"]}, {"table": "ods.ods_campaigns_df", "columns": ["organization_name", "foreign", "head"]}], "writes": [{"table": "defensespending", "columns": ["organization_name", "foreign", "head"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO co2emissions SELECT mean_temperature_f, manufacturername, distance FROM airport_aircraft WHERE mean_temperature_f > 187\");\n", "labels": {"reads": [{"table": "airport_aircraft", "columns": ["mean_temperature_f", "manufacturername", "distance"]}], "writes": [{"table": "co2emissions", "columns": ["mean_temperature_f", "manufacturername", "distance"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO trenches SELECT race, disaster_id, party, plant_location FROM teacher_development_race WHERE race > 478\"\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": ["race", "disaster_id", "party", "plant_location"]}], "writes": [{"table": "trenches", "columns": ["race", "disaster_id", "party", "plant_location"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table review --columns spacecraftid,primary --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "review", "columns": ["spacecraftid", "primary"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT clinic_type, request FROM ref_calendar LIMIT 247\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO government.region SELECT countryid, workout_name, trend FROM audience WHERE countryid > 491\")\n", "labels": {"reads": [{"table": "ref_calendar", "columns": ["clinic_type", "request"]}, {"table": "audience", "columns": ["countryid", "workout_name", "trend"]}], "writes": [{"table": "government.region", "columns": ["countryid", "workout_name", "trend"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO taj_mahal_visitors SELECT implementation_year, contract_id FROM ads_sessions_di WHERE implementation_year > 59\"], check=True)\n", "labels": {"reads": [{"table": "ads_sessions_di", "columns": ["implementation_year", "contract_id"]}], "writes": [{"table": "taj_mahal_visitors", "columns": ["implementation_year", "contract_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table art_pieces --target-dir /tmp/land\n", "labels": {"reads": [{"table": "art_pieces", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT used_kb, pname FROM section\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"ads.payments_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "section", "columns": ["used_kb", "pname"]}], "writes": [{"table": "ads.payments_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO landfillcapacitybycountry SELECT * FROM legacy\ncur.execute(\"SELECT insurancetype, characteristic_data_type FROM bi.bi_events_daily LIMIT 369\")\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": ["insurancetype", "characteristic_data_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 466;\nEOF\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": ["practice_id", "life_expectancy"]}], "writes": [{"table": "testtypes", "columns": ["practice_id", "life_expectancy"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"space_programs\").where(\"dt = current_date()\").writeTo(\"dwd.dwd_exposure_df\").append()\n", "labels": {"reads": [{"table": "space_programs", "columns": null}], "writes": [{"table": "dwd.dwd_exposure_df", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO projecttimeline (forename, follow_up_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "projecttimeline", "columns": ["forename", "follow_up_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO item_prices SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table studentaccommodations --columns date_in_locaton_to,strat_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "studentaccommodations", "columns": ["date_in_locaton_to", "strat_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"courts\")\nsrc.write.insertInto(\"dws_events_df\", overwrite=True)\n", "labels": {"reads": [{"table": "courts", "columns": null}], "writes": [{"table": "dws_events_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table taj_mahal_visitors --target-dir /tmp/land\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO channel SELECT characteristic_type_code, lifespan, workshop_group_id, bioprocess_name FROM gymnast WHERE characteristic_type_code > 342\")\n", "labels": {"reads": [{"table": "gymnast", "columns": ["characteristic_type_code", "lifespan", "workshop_group_id", "bioprocess_name"]}], "writes": [{"table": "channel", "columns": ["characteristic_type_code", "lifespan", "workshop_group_id", "bioprocess_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO seeds SELECT likes, last_workout_date, retweets FROM freightforwarding WHERE likes > 475\"\n", "labels": {"reads": [{"table": "freightforwarding", "columns": ["likes", "last_workout_date", "retweets"]}], "writes": [{"table": "seeds", "columns": ["likes", "last_workout_date", "retweets"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO biosensors SELECT report_type, paintingid, claimdate FROM ads.ads_events_df WHERE report_type > 493\")\n", "labels": {"reads": [{"table": "ads.ads_events_df", "columns": ["report_type", "paintingid", "claimdate"]}], "writes": [{"table": "biosensors", "columns": ["report_type", "paintingid", "claimdate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dysprosiumproduction SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_of_completion, ingredient FROM op_projects LIMIT 304\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "op_projects", "columns": ["date_of_completion", "ingredient"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 176;\nSQL\n", "labels": {"reads": [{"table": "travel_advisory", "columns": ["pages_per_minute_color", "donation_year"]}, {"table": "airport", "columns": ["units_owned", "workout_id"]}], "writes": [{"table": "shipmentinfo", "columns": ["units_owned", "workout_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO research SELECT * FROM legacy\ncur.execute(\"SELECT stu_gpa, author FROM participates_in LIMIT 200\")\n", "labels": {"reads": [{"table": "participates_in", "columns": ["stu_gpa", "author"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg_payments_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"container\")\n", "labels": {"reads": [{"table": "stg_payments_hourly", "columns": null}], "writes": [{"table": "container", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT pressure, mode FROM ods.sessions LIMIT 420\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "ods.sessions", "columns": ["pressure", "mode"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO rural_hospitals SELECT don_id, sitename FROM beauty_products WHERE don_id > 498\"\n", "labels": {"reads": [{"table": "beauty_products", "columns": ["don_id", "sitename"]}], "writes": [{"table": "rural_hospitals", "columns": ["don_id", "sitename"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"hockey_players\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "hockey_players", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO causes_insert_2 SELECT volunteername, violationtype, date_account_opened, user_name FROM lawyers WHERE volunteername > 14\")\n", "labels": {"reads": [{"table": "lawyers", "columns": ["volunteername", "violationtype", "date_account_opened", "user_name"]}], "writes": [{"table": "causes_insert_2", "columns": ["volunteername", "violationtype", "date_account_opened", "user_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table shariah_compliant_products --columns owner_id,usage --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "shariah_compliant_products", "columns": ["owner_id", "usage"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO wastewater_treatment SELECT cruelty_free, total_spent, dept_name FROM dws_cart_item WHERE cruelty_free > 190\");\n", "labels": {"reads": [{"table": "dws_cart_item", "columns": ["cruelty_free", "total_spent", "dept_name"]}], "writes": [{"table": "wastewater_treatment", "columns": ["cruelty_free", "total_spent", "dept_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_input(ctx, \"publication\")\nwrite_to_output(df, \"dwd.dwd_cart_item_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "publication", "columns": null}], "writes": [{"table": "dwd.dwd_cart_item_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO artist_demographics SELECT continent_id, participant_name, vice_president_vote, train_id FROM ocean_floor WHERE continent_id > 105\");\n", "labels": {"reads": [{"table": "ocean_floor", "columns": ["continent_id", "participant_name", "vice_president_vote", "train_id"]}], "writes": [{"table": "artist_demographics", "columns": ["continent_id", "participant_name", "vice_president_vote", "train_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"vehicle_data\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"season_assists\")\n", "labels": {"reads": [{"table": "vehicle_data", "columns": null}], "writes": [{"table": "season_assists", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model clicks_di depends on mediterranean_salinity\ndbt run --select clicks_di --vars '{\"src\":\"mediterranean_salinity\"}'\n", "labels": {"reads": [{"table": "mediterranean_salinity", "columns": null}], "writes": [{"table": "clicks_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"contract_transactions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "contract_transactions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 141;\nEOF\n", "labels": {"reads": [{"table": "ocean_depths", "columns": ["temporary_acting", "suppliername"]}], "writes": [{"table": "military_innovation", "columns": ["temporary_acting", "suppliername"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO co2_emissions (pd_id, request_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "co2_emissions", "columns": ["pd_id", "request_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.copy_number > 499).all()\n# src table: trees\nengine.execute(\"INSERT INTO recycling_rates_state SELECT * FROM trees\")\n", "labels": {"reads": [{"table": "trees", "columns": null}], "writes": [{"table": "recycling_rates_state", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"district_schools\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "district_schools", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 19;\nSQL\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": ["creationyear", "temperature"]}, {"table": "safetyorgs", "columns": ["local", "is_unionized", "garment_type", "sustainability_certified"]}], "writes": [{"table": "model_fairness", "columns": ["local", "is_unionized", "garment_type", "sustainability_certified"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO festivals (feature_id, all_home) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "festivals", "columns": ["feature_id", "all_home"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO organic_cosmetics SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO songs SELECT * FROM legacy\nspark.sql(\"INSERT INTO concert_revenue SELECT acidity, media_type_id FROM supplier_addresses WHERE acidity > 161\")\n", "labels": {"reads": [{"table": "supplier_addresses", "columns": ["acidity", "media_type_id"]}], "writes": [{"table": "concert_revenue", "columns": ["acidity", "media_type_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"item_prices\");\ndf.write().mode(\"overwrite\").saveAsTable(\"student_tests_taken\");\n", "labels": {"reads": [{"table": "item_prices", "columns": null}], "writes": [{"table": "student_tests_taken", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO militarycyberops SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamedesigndata\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"garmentproduction\")\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": null}], "writes": [{"table": "garmentproduction", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO network_infrastructure SELECT farmer_id, home_team_points FROM broadband_plans WHERE farmer_id > 420\"\n", "labels": {"reads": [{"table": "broadband_plans", "columns": ["farmer_id", "home_team_points"]}], "writes": [{"table": "network_infrastructure", "columns": ["farmer_id", "home_team_points"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO publications SELECT neighborhoodname, working_horses, male_id FROM talent_acquisition WHERE neighborhoodname > 38\"\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": ["neighborhoodname", "working_horses", "male_id"]}], "writes": [{"table": "publications", "columns": ["neighborhoodname", "working_horses", "male_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO economic_diversification_efforts (tripid, max_gust_speed_mph) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "economic_diversification_efforts", "columns": ["tripid", "max_gust_speed_mph"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"civilcases\").where(\"dt = current_date()\").writeTo(\"decentralized_applications\").append()\n", "labels": {"reads": [{"table": "civilcases", "columns": null}], "writes": [{"table": "decentralized_applications", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO unesco_intangible_heritage (media_type_id, artpiecename) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "unesco_intangible_heritage", "columns": ["media_type_id", "artpiecename"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 121;\nEOF\n", "labels": {"reads": [{"table": "organisations", "columns": ["priceid", "contract_value", "mentalhealthscore", "openning_year"]}], "writes": [{"table": "astronaut_missions", "columns": ["priceid", "contract_value", "mentalhealthscore", "openning_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO courtcases (exit_type, startup_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "courtcases", "columns": ["exit_type", "startup_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewable_projects\").toPandas()\ndf[[\"record_id\", \"hub_id\"]].to_sql(\"farm_competition\", engine, index=False)\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}], "writes": [{"table": "farm_competition", "columns": ["record_id", "hub_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO communityengagements SELECT * FROM legacy\nspark.sql(\"INSERT INTO collective_bargaining SELECT cb_year, waste_generation, port_name FROM mine WHERE cb_year > 393\")\n", "labels": {"reads": [{"table": "mine", "columns": ["cb_year", "waste_generation", "port_name"]}], "writes": [{"table": "collective_bargaining", "columns": ["cb_year", "waste_generation", "port_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO daily_industrial_water_usage SELECT university, plantid, count_time, num_stops FROM match_result WHERE university > 99\");\n", "labels": {"reads": [{"table": "match_result", "columns": ["university", "plantid", "count_time", "num_stops"]}], "writes": [{"table": "daily_industrial_water_usage", "columns": ["university", "plantid", "count_time", "num_stops"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dysprosiumproduction\").toPandas()\ndf[[\"scientist\", \"projectid\"]].to_sql(\"economic_diversification\", engine, index=False)\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": null}], "writes": [{"table": "economic_diversification", "columns": ["scientist", "projectid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO regular_order_products (cultivatorname, field) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "regular_order_products", "columns": ["cultivatorname", "field"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 458;\nSQL\n", "labels": {"reads": [{"table": "arctictemperature", "columns": ["porphyria", "policytype"]}, {"table": "redundant_billing_data", "columns": ["date_from", "creator", "primary_conference", "dept_store_chain_id"]}], "writes": [{"table": "people_addresses", "columns": ["date_from", "creator", "primary_conference", "dept_store_chain_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"unionnegotiations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"art_exhibit_attendance\")\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": null}], "writes": [{"table": "art_exhibit_attendance", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.protein_name > 362).all()\n# src table: dws.risk_score_daily\nengine.execute(\"INSERT INTO chemical_production_5 SELECT * FROM dws.risk_score_daily\")\n", "labels": {"reads": [{"table": "dws.risk_score_daily", "columns": null}], "writes": [{"table": "chemical_production_5", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT is_eco_friendly, sighting_date FROM directors\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"tv_shows_genre\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "directors", "columns": ["is_eco_friendly", "sighting_date"]}], "writes": [{"table": "tv_shows_genre", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO vendorfabrics SELECT addressid, detected_at, date_of_ceremony, incidenttype FROM staff_department_assignments WHERE addressid > 451\")\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["addressid", "detected_at", "date_of_ceremony", "incidenttype"]}], "writes": [{"table": "vendorfabrics", "columns": ["addressid", "detected_at", "date_of_ceremony", "incidenttype"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO mart.clicks_delta SELECT a.total_attendance, b.level FROM tb_cases a JOIN bi.member_point_full b ON a.billing_amount = b.billing_amount\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tb_cases", "columns": null}, {"table": "bi.member_point_full", "columns": null}], "writes": [{"table": "mart.clicks_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT catalog_entry_name, num_songs FROM ods.ods_exposure_delta LIMIT 296\")\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ocean_depths SELECT rural_area, opening_year, last_workout_date, cause FROM stellar_transactions WHERE rural_area > 393\")\n", "labels": {"reads": [{"table": "ods.ods_exposure_delta", "columns": ["catalog_entry_name", "num_songs"]}, {"table": "stellar_transactions", "columns": ["rural_area", "opening_year", "last_workout_date", "cause"]}], "writes": [{"table": "ocean_depths", "columns": ["rural_area", "opening_year", "last_workout_date", "cause"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO incident (policy_name, trench_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "incident", "columns": ["policy_name", "trench_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"market_access\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"explainableai\")\n", "labels": {"reads": [{"table": "market_access", "columns": null}], "writes": [{"table": "explainableai", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.mart_shipments_hourly SELECT shipped_to, practiceid FROM museums WHERE shipped_to > 34\"], check=True)\n", "labels": {"reads": [{"table": "museums", "columns": ["shipped_to", "practiceid"]}], "writes": [{"table": "mart.mart_shipments_hourly", "columns": ["shipped_to", "practiceid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.risk_score_di\").toPandas()\ndf[[\"overall_rating\", \"saleamount\"]].to_sql(\"artsandcrafts\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": null}], "writes": [{"table": "artsandcrafts", "columns": ["overall_rating", "saleamount"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM country_labor\", conn)\ndf.to_sql(\"traditional_arts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "country_labor", "columns": null}], "writes": [{"table": "traditional_arts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO galleries SELECT updated_at, work_type, document_type_name FROM student_addresses WHERE updated_at > 486\")\n", "labels": {"reads": [{"table": "student_addresses", "columns": ["updated_at", "work_type", "document_type_name"]}], "writes": [{"table": "galleries", "columns": ["updated_at", "work_type", "document_type_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO member_data SELECT incidents, kids, cultural_diversity FROM companies_extended WHERE incidents > 118\")\n", "labels": {"reads": [{"table": "companies_extended", "columns": ["incidents", "kids", "cultural_diversity"]}], "writes": [{"table": "member_data", "columns": ["incidents", "kids", "cultural_diversity"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO program_budget SELECT programname, eventattendance, measurement, max_wind_speed_mph FROM preferences WHERE programname > 108\"\n", "labels": {"reads": [{"table": "preferences", "columns": ["programname", "eventattendance", "measurement", "max_wind_speed_mph"]}], "writes": [{"table": "program_budget", "columns": ["programname", "eventattendance", "measurement", "max_wind_speed_mph"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO electronics_factories SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.users_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.risk_score\")\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": null}], "writes": [{"table": "ads.risk_score", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table flights --columns stars,assists --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "flights", "columns": ["stars", "assists"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table head --target-dir /tmp/land\n", "labels": {"reads": [{"table": "head", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO shared_ebikes SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"menuitems\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "menuitems", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO park SELECT a.athleteid, b.salinity FROM university a JOIN course_attendance b ON a.station_name = b.station_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "university", "columns": null}, {"table": "course_attendance", "columns": null}], "writes": [{"table": "park", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO tech_workers_union SELECT hospital_id, job_category, max_dew_point_f, to_address FROM stg.stg_exposure_daily WHERE hospital_id > 412\");\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": ["hospital_id", "job_category", "max_dew_point_f", "to_address"]}], "writes": [{"table": "tech_workers_union", "columns": ["hospital_id", "job_category", "max_dew_point_f", "to_address"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"county\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"vehicle_maintenance\")\n", "labels": {"reads": [{"table": "county", "columns": null}], "writes": [{"table": "vehicle_maintenance", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_exposure_delta SELECT location_code, reservoir_id FROM singer WHERE location_code > 472\"\n", "labels": {"reads": [{"table": "singer", "columns": ["location_code", "reservoir_id"]}], "writes": [{"table": "ods.ods_exposure_delta", "columns": ["location_code", "reservoir_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT accessible, attraction_name FROM gameplatforms LIMIT 441\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "gameplatforms", "columns": ["accessible", "attraction_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"individual\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "individual", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO parts (contributorid, min_depth) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "parts", "columns": ["contributorid", "min_depth"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO waterconsumptionbyoperation SELECT contributorid, lawyer_name, fair_trade, country FROM space_agencies_2 WHERE contributorid > 263\"\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": ["contributorid", "lawyer_name", "fair_trade", "country"]}], "writes": [{"table": "waterconsumptionbyoperation", "columns": ["contributorid", "lawyer_name", "fair_trade", "country"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ocean_shipping.cargo SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"marine_life_populations\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"satellites_in_orbit\");\ndf.write().mode(\"overwrite\").saveAsTable(\"emerging_markets.digital_assets\");\n", "labels": {"reads": [{"table": "satellites_in_orbit", "columns": null}], "writes": [{"table": "emerging_markets.digital_assets", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"bi.inventory_delta\")\nsave_to_target(df, \"shoes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.inventory_delta", "columns": null}], "writes": [{"table": "shoes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dwd.dwd_campaigns_df SELECT a.vulnerability, b.strat_id FROM student_program_mapping a JOIN biotech.startups b ON a.other_characteristic_details = b.other_characteristic_details\"\n", "labels": {"reads": [{"table": "student_program_mapping", "columns": null}, {"table": "biotech.startups", "columns": null}], "writes": [{"table": "dwd.dwd_campaigns_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO green_buildings_us SELECT startdate, level FROM management WHERE startdate > 450\"\n", "labels": {"reads": [{"table": "management", "columns": ["startdate", "level"]}], "writes": [{"table": "green_buildings_us", "columns": ["startdate", "level"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO english_premier_league SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT leadershiptraining, ethical_certifications FROM pipelines\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"incidents\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "pipelines", "columns": ["leadershiptraining", "ethical_certifications"]}], "writes": [{"table": "incidents", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads.ads_campaigns_full SELECT a.birthdate, b.dept_code FROM electric_buses a JOIN military_personnel_africa b ON a.market_share = b.market_share\"\n", "labels": {"reads": [{"table": "electric_buses", "columns": null}, {"table": "military_personnel_africa", "columns": null}], "writes": [{"table": "ads.ads_campaigns_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO evsales SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vr_tech SELECT a.store_id, b.journal_id FROM artwork a JOIN schools b ON a.borough = b.borough\"\n", "labels": {"reads": [{"table": "artwork", "columns": null}, {"table": "schools", "columns": null}], "writes": [{"table": "vr_tech", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ads.vendors_delta\");\ndf.write().mode(\"overwrite\").saveAsTable(\"vessel_types\");\n", "labels": {"reads": [{"table": "ads.vendors_delta", "columns": null}], "writes": [{"table": "vessel_types", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO rural_projects SELECT a.product_category_code, b.goldid FROM bi.bi_inventory a JOIN rural_resources b ON a.date_from = b.date_from\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi.bi_inventory", "columns": null}, {"table": "rural_resources", "columns": null}], "writes": [{"table": "rural_projects", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM hotel_reviews\"\n", "labels": {"reads": [{"table": "hotel_reviews", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO india_ingredient_sourcing SELECT customer_id, average, product_stock_number FROM seeds WHERE customer_id > 65\")\n", "labels": {"reads": [{"table": "seeds", "columns": ["customer_id", "average", "product_stock_number"]}], "writes": [{"table": "india_ingredient_sourcing", "columns": ["customer_id", "average", "product_stock_number"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO accommodations SELECT workout_type, length_feet, driver_id FROM ecohousing WHERE workout_type > 491\");\n", "labels": {"reads": [{"table": "ecohousing", "columns": ["workout_type", "length_feet", "driver_id"]}], "writes": [{"table": "accommodations", "columns": ["workout_type", "length_feet", "driver_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.mart_users_df SELECT * FROM legacy\ncur.execute(\"SELECT white, production_mwh FROM genre LIMIT 266\")\n", "labels": {"reads": [{"table": "genre", "columns": ["white", "production_mwh"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO stellar_transactions SELECT appointment_duration, brand_name, user_account, dept_store_id FROM hires WHERE appointment_duration > 337\")\n", "labels": {"reads": [{"table": "hires", "columns": ["appointment_duration", "brand_name", "user_account", "dept_store_id"]}], "writes": [{"table": "stellar_transactions", "columns": ["appointment_duration", "brand_name", "user_account", "dept_store_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 146;\nEOF\n", "labels": {"reads": [{"table": "sector_incidents", "columns": ["premise_id", "home_team", "destination"]}], "writes": [{"table": "innovation_metrics", "columns": ["premise_id", "home_team", "destination"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO defense_projects SELECT doctorsper1000, target_id, fund_name FROM workout_data WHERE doctorsper1000 > 357\");\n", "labels": {"reads": [{"table": "workout_data", "columns": ["doctorsper1000", "target_id", "fund_name"]}], "writes": [{"table": "defense_projects", "columns": ["doctorsper1000", "target_id", "fund_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO strandings SELECT water_consumption, eco_certified, share_in_percent, capital FROM stellar_transactions WHERE water_consumption > 273\");\n", "labels": {"reads": [{"table": "stellar_transactions", "columns": ["water_consumption", "eco_certified", "share_in_percent", "capital"]}], "writes": [{"table": "strandings", "columns": ["water_consumption", "eco_certified", "share_in_percent", "capital"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"station_company\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "station_company", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mining_companies SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM pipelines\", conn)\ndf.to_sql(\"fraud_detections\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "pipelines", "columns": null}], "writes": [{"table": "fraud_detections", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"market_share\")\nsrc.write.insertInto(\"ads_sessions_di\", overwrite=True)\n", "labels": {"reads": [{"table": "market_share", "columns": null}], "writes": [{"table": "ads_sessions_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO fleet_management SELECT * FROM legacy\nspark.sql(\"INSERT INTO mart.mart_coupon_use_full SELECT publication_id, group_name, build_year FROM gamedata WHERE publication_id > 14\")\n", "labels": {"reads": [{"table": "gamedata", "columns": ["publication_id", "group_name", "build_year"]}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": ["publication_id", "group_name", "build_year"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"classicgame\").toPandas()\ndf[[\"premise_details\", \"productionrate\"]].to_sql(\"dws.shipments_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": [{"table": "dws.shipments_daily", "columns": ["premise_details", "productionrate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO schools SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO mediterranean_salinity SELECT debate_id, dose, professional_development FROM digital_trends WHERE debate_id > 248\");\n", "labels": {"reads": [{"table": "digital_trends", "columns": ["debate_id", "dose", "professional_development"]}], "writes": [{"table": "mediterranean_salinity", "columns": ["debate_id", "dose", "professional_development"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO creativeais SELECT local, claimdate FROM seamounts WHERE local > 64\");\n", "labels": {"reads": [{"table": "seamounts", "columns": ["local", "claimdate"]}], "writes": [{"table": "creativeais", "columns": ["local", "claimdate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO weekly_weather SELECT order_status, rehab_date FROM competition WHERE order_status > 14\");\n", "labels": {"reads": [{"table": "competition", "columns": ["order_status", "rehab_date"]}], "writes": [{"table": "weekly_weather", "columns": ["order_status", "rehab_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table department_store_chain --columns school_name,stageposition --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "department_store_chain", "columns": ["school_name", "stageposition"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sea, part_id FROM dwd.campaigns LIMIT 193\")\nimport logging\nspark.sql(\"INSERT INTO construction_labor_stats SELECT chair_name, target_u_id FROM drivers WHERE chair_name > 365\")\n", "labels": {"reads": [{"table": "dwd.campaigns", "columns": ["sea", "part_id"]}, {"table": "drivers", "columns": ["chair_name", "target_u_id"]}], "writes": [{"table": "construction_labor_stats", "columns": ["chair_name", "target_u_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"prison\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "prison", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO appellations SELECT revenue, oct, mappingid, petid FROM casesbyyear WHERE revenue > 167\"\n", "labels": {"reads": [{"table": "casesbyyear", "columns": ["revenue", "oct", "mappingid", "petid"]}], "writes": [{"table": "appellations", "columns": ["revenue", "oct", "mappingid", "petid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table stg.stg_risk_score_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"birds\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "birds", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO vessel_types (has_disability, program_category) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "vessel_types", "columns": ["has_disability", "program_category"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO college SELECT attendance_id, watertemp, case_burden, building_type FROM buildings WHERE attendance_id > 279\");\n", "labels": {"reads": [{"table": "buildings", "columns": ["attendance_id", "watertemp", "case_burden", "building_type"]}], "writes": [{"table": "college", "columns": ["attendance_id", "watertemp", "case_burden", "building_type"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws_products (stocking_density, waste_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws_products", "columns": ["stocking_density", "waste_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO concerts SELECT director, founder_identity FROM satellites WHERE director > 479\")\n", "labels": {"reads": [{"table": "satellites", "columns": ["director", "founder_identity"]}], "writes": [{"table": "concerts", "columns": ["director", "founder_identity"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_member_point_di\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tickets\")\n", "labels": {"reads": [{"table": "dws.dws_member_point_di", "columns": null}], "writes": [{"table": "tickets", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO garmentproduction SELECT * FROM legacy\nspark.sql(\"INSERT INTO has_amenity SELECT duration_ms, away_team_id, account_number FROM culturalevents WHERE duration_ms > 296\")\n", "labels": {"reads": [{"table": "culturalevents", "columns": ["duration_ms", "away_team_id", "account_number"]}], "writes": [{"table": "has_amenity", "columns": ["duration_ms", "away_team_id", "account_number"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.manufacturername > 391).all()\n# src table: disinformation_detection\nengine.execute(\"INSERT INTO protein SELECT * FROM disinformation_detection\")\n", "labels": {"reads": [{"table": "disinformation_detection", "columns": null}], "writes": [{"table": "protein", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ship_agent SELECT advisory_id, activity, workers FROM ref_document_status WHERE advisory_id > 382\"], check=True)\n", "labels": {"reads": [{"table": "ref_document_status", "columns": ["advisory_id", "activity", "workers"]}], "writes": [{"table": "ship_agent", "columns": ["advisory_id", "activity", "workers"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO healthequitymetrics SELECT home_team_three_point, event, veteran_id, drug FROM dwd.products_hourly WHERE home_team_three_point > 9\"\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": ["home_team_three_point", "event", "veteran_id", "drug"]}], "writes": [{"table": "healthequitymetrics", "columns": ["home_team_three_point", "event", "veteran_id", "drug"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO guests SELECT trench_id, goal_date, case_number FROM fabricinventory WHERE trench_id > 260\");\n", "labels": {"reads": [{"table": "fabricinventory", "columns": ["trench_id", "goal_date", "case_number"]}], "writes": [{"table": "guests", "columns": ["trench_id", "goal_date", "case_number"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hotel_chain_id, activity FROM flight_safety LIMIT 440\")\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO casesbyyear SELECT creation, supply_volume, aid_id FROM consumer_preference WHERE creation > 13\")\n", "labels": {"reads": [{"table": "flight_safety", "columns": ["hotel_chain_id", "activity"]}, {"table": "consumer_preference", "columns": ["creation", "supply_volume", "aid_id"]}], "writes": [{"table": "casesbyyear", "columns": ["creation", "supply_volume", "aid_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT menuname, safety_score FROM languages LIMIT 129\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO aquatic_farms SELECT awardid, diversity_score, nominee, mental_health_resource_access FROM bi.bi_inventory_delta WHERE awardid > 370\")\n", "labels": {"reads": [{"table": "languages", "columns": ["menuname", "safety_score"]}, {"table": "bi.bi_inventory_delta", "columns": ["awardid", "diversity_score", "nominee", "mental_health_resource_access"]}], "writes": [{"table": "aquatic_farms", "columns": ["awardid", "diversity_score", "nominee", "mental_health_resource_access"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO passenger_trips SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT goldid, department_id FROM testtypes LIMIT 183\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "testtypes", "columns": ["goldid", "department_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO economic_diversification SELECT wrestler_id, borough, program_expenses, physician FROM sustainable_projects WHERE wrestler_id > 150\"\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["wrestler_id", "borough", "program_expenses", "physician"]}], "writes": [{"table": "economic_diversification", "columns": ["wrestler_id", "borough", "program_expenses", "physician"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 80;\nEOF\n", "labels": {"reads": [{"table": "vulnerabilities", "columns": ["bookings", "game_genre"]}], "writes": [{"table": "item", "columns": ["bookings", "game_genre"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"operate_company\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "operate_company", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ca_menu_items SELECT laborproductivity, student_id, resource_type, hospital_id FROM shared_rides_tokyo WHERE laborproductivity > 451\"\n", "labels": {"reads": [{"table": "shared_rides_tokyo", "columns": ["laborproductivity", "student_id", "resource_type", "hospital_id"]}], "writes": [{"table": "ca_menu_items", "columns": ["laborproductivity", "student_id", "resource_type", "hospital_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table traffic_violations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "traffic_violations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"systems\")\nsrc.write.insertInto(\"circulation_history\", overwrite=True)\n", "labels": {"reads": [{"table": "systems", "columns": null}], "writes": [{"table": "circulation_history", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nimport logging\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO bus_routes SELECT a.userid, b.number_cities FROM government.city a JOIN causes_insert_2 b ON a.principal_activities = b.principal_activities\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "government.city", "columns": null}, {"table": "causes_insert_2", "columns": null}], "writes": [{"table": "bus_routes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nimport logging\nsql = \"INSERT INTO providers SELECT a.attribute_id, b.workout_name FROM mart_payments_df a JOIN vehiclemodels b ON a.publication_id = b.publication_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mart_payments_df", "columns": null}, {"table": "vehiclemodels", "columns": null}], "writes": [{"table": "providers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO investors SELECT a.garment_material, b.contract_amount FROM date a JOIN performance_scores b ON a.satelliteid = b.satelliteid\"\n", "labels": {"reads": [{"table": "date", "columns": null}, {"table": "performance_scores", "columns": null}], "writes": [{"table": "investors", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"environmental_impact_stats\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"labor_cost\")\n", "labels": {"reads": [{"table": "environmental_impact_stats", "columns": null}], "writes": [{"table": "labor_cost", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table league_x --columns royal_family_details,contract_address --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "league_x", "columns": ["royal_family_details", "contract_address"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"production_yearly\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"bi.bi_payments_delta\")\n", "labels": {"reads": [{"table": "production_yearly", "columns": null}], "writes": [{"table": "bi.bi_payments_delta", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart_campaigns_delta SELECT * FROM legacy\ncur.execute(\"SELECT vaccine_type, hardware_model_name FROM draft_copies LIMIT 93\")\n", "labels": {"reads": [{"table": "draft_copies", "columns": ["vaccine_type", "hardware_model_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO department_stores SELECT fundingdate, max_dew_point_f FROM green_building_projects WHERE fundingdate > 262\"], check=True)\n", "labels": {"reads": [{"table": "green_building_projects", "columns": ["fundingdate", "max_dew_point_f"]}], "writes": [{"table": "department_stores", "columns": ["fundingdate", "max_dew_point_f"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT address, sale_year FROM firestations LIMIT 300\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO shariah_compliant_finance SELECT employee_id, completion_status, contractorid, billing FROM communities WHERE employee_id > 294\")\n", "labels": {"reads": [{"table": "firestations", "columns": ["address", "sale_year"]}, {"table": "communities", "columns": ["employee_id", "completion_status", "contractorid", "billing"]}], "writes": [{"table": "shariah_compliant_finance", "columns": ["employee_id", "completion_status", "contractorid", "billing"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT production_date, component_type FROM dw.member_point_daily LIMIT 21\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO drug_sales SELECT floor_area_m2, amount_settled, calories FROM documents WHERE floor_area_m2 > 448\")\n", "labels": {"reads": [{"table": "dw.member_point_daily", "columns": ["production_date", "component_type"]}, {"table": "documents", "columns": ["floor_area_m2", "amount_settled", "calories"]}], "writes": [{"table": "drug_sales", "columns": ["floor_area_m2", "amount_settled", "calories"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dwd.dwd_orders_di SELECT * FROM legacy\ncur.execute(\"SELECT tot_cred, system FROM highest_scores LIMIT 209\")\n", "labels": {"reads": [{"table": "highest_scores", "columns": ["tot_cred", "system"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stay\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "stay", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"financialwellbeing\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "financialwellbeing", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO makeup_products SELECT concert_name, invested FROM product_ingredient WHERE concert_name > 16\"\n", "labels": {"reads": [{"table": "product_ingredient", "columns": ["concert_name", "invested"]}], "writes": [{"table": "makeup_products", "columns": ["concert_name", "invested"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dw.inventory_delta (disaster_id, shipmenttype) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dw.inventory_delta", "columns": ["disaster_id", "shipmenttype"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO product SELECT launched_year, subject, volunteer_name FROM dancefunding WHERE launched_year > 421\"\n", "labels": {"reads": [{"table": "dancefunding", "columns": ["launched_year", "subject", "volunteer_name"]}], "writes": [{"table": "product", "columns": ["launched_year", "subject", "volunteer_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO posts (affirmative, draft_details) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "posts", "columns": ["affirmative", "draft_details"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO pharmasales SELECT * FROM legacy\nspark.sql(\"INSERT INTO space_missions SELECT bdate, screening, color_code, sanctuary FROM sustainabilityratings WHERE bdate > 42\")\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": ["bdate", "screening", "color_code", "sanctuary"]}], "writes": [{"table": "space_missions", "columns": ["bdate", "screening", "color_code", "sanctuary"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table disinformation_detection --columns socialimpactscore,ethical_certifications --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "disinformation_detection", "columns": ["socialimpactscore", "ethical_certifications"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table community_policing --columns artistid,regulation --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "community_policing", "columns": ["artistid", "regulation"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO coowners SELECT a.mission_name, b.complaint_status_code FROM languagesatrisk a JOIN fair_wages b ON a.cmi_details = b.cmi_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": null}, {"table": "fair_wages", "columns": null}], "writes": [{"table": "coowners", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT feb, alid FROM plays_games LIMIT 142\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO na_schema.hospitals SELECT date_payment_made, enable_dm, jan, author_community FROM region WHERE date_payment_made > 108\")\n", "labels": {"reads": [{"table": "plays_games", "columns": ["feb", "alid"]}, {"table": "region", "columns": ["date_payment_made", "enable_dm", "jan", "author_community"]}], "writes": [{"table": "na_schema.hospitals", "columns": ["date_payment_made", "enable_dm", "jan", "author_community"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_shipments_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"fireincidents\")\n", "labels": {"reads": [{"table": "bi_shipments_daily", "columns": null}], "writes": [{"table": "fireincidents", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO complaints SELECT sales_id, material, day_of_week FROM dwd.dwd_campaigns WHERE sales_id > 15\");\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns", "columns": ["sales_id", "material", "day_of_week"]}], "writes": [{"table": "complaints", "columns": ["sales_id", "material", "day_of_week"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"aquatic_farms\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"projecttimelinebybudget\")\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": null}], "writes": [{"table": "projecttimelinebybudget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO restaurants SELECT * FROM legacy\ncur.execute(\"SELECT party, art_movement FROM lenders LIMIT 58\")\n", "labels": {"reads": [{"table": "lenders", "columns": ["party", "art_movement"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ocean_basins SELECT hospitalname, supplier, amount_settled FROM tourism_activities WHERE hospitalname > 472\"], check=True)\n", "labels": {"reads": [{"table": "tourism_activities", "columns": ["hospitalname", "supplier", "amount_settled"]}], "writes": [{"table": "ocean_basins", "columns": ["hospitalname", "supplier", "amount_settled"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO enrollments SELECT scooter_id, district, production_qty FROM makeup_products WHERE scooter_id > 389\")\n", "labels": {"reads": [{"table": "makeup_products", "columns": ["scooter_id", "district", "production_qty"]}], "writes": [{"table": "enrollments", "columns": ["scooter_id", "district", "production_qty"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO parking_fines SELECT * FROM legacy\ncur.execute(\"SELECT yield_id, topic FROM genre LIMIT 170\")\n", "labels": {"reads": [{"table": "genre", "columns": ["yield_id", "topic"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO port_office SELECT reason, sales_billion FROM deep_sea_expeditions WHERE reason > 492\");\n", "labels": {"reads": [{"table": "deep_sea_expeditions", "columns": ["reason", "sales_billion"]}], "writes": [{"table": "port_office", "columns": ["reason", "sales_billion"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM systems\", conn)\ndf.to_sql(\"membership_data\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "systems", "columns": null}], "writes": [{"table": "membership_data", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bike_share\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "bike_share", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO ref_attraction_types SELECT prereq_id, distance, customerid, saleamount FROM eco_materials WHERE prereq_id > 404\")\n", "labels": {"reads": [{"table": "eco_materials", "columns": ["prereq_id", "distance", "customerid", "saleamount"]}], "writes": [{"table": "ref_attraction_types", "columns": ["prereq_id", "distance", "customerid", "saleamount"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dw.dw_orders_hourly\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dw.dw_orders_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO threats SELECT policy_name, founder_ethnicity, hosts, ngo_name FROM underwater_trenches WHERE policy_name > 208\");\n", "labels": {"reads": [{"table": "underwater_trenches", "columns": ["policy_name", "founder_ethnicity", "hosts", "ngo_name"]}], "writes": [{"table": "threats", "columns": ["policy_name", "founder_ethnicity", "hosts", "ngo_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"disaster_response\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "disaster_response", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO battery_storage SELECT feedid, is_vegan, recorded_by_staff_id, catalog_level_name FROM defense_contractors WHERE feedid > 72\");\n", "labels": {"reads": [{"table": "defense_contractors", "columns": ["feedid", "is_vegan", "recorded_by_staff_id", "catalog_level_name"]}], "writes": [{"table": "battery_storage", "columns": ["feedid", "is_vegan", "recorded_by_staff_id", "catalog_level_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fish_farms SELECT built_year, hispanic, member, subscriber_type FROM dwd.coupon_use_daily WHERE built_year > 471\"\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": ["built_year", "hispanic", "member", "subscriber_type"]}], "writes": [{"table": "fish_farms", "columns": ["built_year", "hispanic", "member", "subscriber_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"public.developers\")\nsrc.write.insertInto(\"movie_financials\", overwrite=True)\n", "labels": {"reads": [{"table": "public.developers", "columns": null}], "writes": [{"table": "movie_financials", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO artist_info SELECT neighborhoodname, line_number, dorm_name FROM stg.stg_products_full WHERE neighborhoodname > 364\")\n", "labels": {"reads": [{"table": "stg.stg_products_full", "columns": ["neighborhoodname", "line_number", "dorm_name"]}], "writes": [{"table": "artist_info", "columns": ["neighborhoodname", "line_number", "dorm_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT manager_name, fare_id FROM producesupplier\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"traditional_arts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "producesupplier", "columns": ["manager_name", "fare_id"]}], "writes": [{"table": "traditional_arts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table bi.bi_risk_score_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.bi_risk_score_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"labor_hours\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"coach\")\n", "labels": {"reads": [{"table": "labor_hours", "columns": null}], "writes": [{"table": "coach", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO renewable_projects SELECT shipping_mode, stockid, pollutant_type FROM clinics_sa WHERE shipping_mode > 369\")\n", "labels": {"reads": [{"table": "clinics_sa", "columns": ["shipping_mode", "stockid", "pollutant_type"]}], "writes": [{"table": "renewable_projects", "columns": ["shipping_mode", "stockid", "pollutant_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT chromosome, user_login FROM safety_incidents_india\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"document_locations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": ["chromosome", "user_login"]}], "writes": [{"table": "document_locations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fireincidents\", conn)\ndf.to_sql(\"defense_project_timelines\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fireincidents", "columns": null}], "writes": [{"table": "defense_project_timelines", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.calendar > 115).all()\n# src table: dws_clicks_di\nengine.execute(\"INSERT INTO iron SELECT * FROM dws_clicks_di\")\n", "labels": {"reads": [{"table": "dws_clicks_di", "columns": null}], "writes": [{"table": "iron", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mining.company SELECT * FROM legacy\ncur.execute(\"SELECT subject_id, menu_type FROM humanitarianassistanceoperations LIMIT 1\")\n", "labels": {"reads": [{"table": "humanitarianassistanceoperations", "columns": ["subject_id", "menu_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO hospital_visits SELECT a.artwork, b.birth_date FROM bi.bi_events_daily a JOIN dws.dws_orders b ON a.publicationid = b.publicationid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": null}, {"table": "dws.dws_orders", "columns": null}], "writes": [{"table": "hospital_visits", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO digital_assets SELECT a.playerid, b.ota_name FROM fairtradecertification a JOIN drug_approval b ON a.matchdate = b.matchdate\"\n", "labels": {"reads": [{"table": "fairtradecertification", "columns": null}, {"table": "drug_approval", "columns": null}], "writes": [{"table": "digital_assets", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"urban_agriculture_initiatives\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "urban_agriculture_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"regular_order_products\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws.dws_events_df\")\n", "labels": {"reads": [{"table": "regular_order_products", "columns": null}], "writes": [{"table": "dws.dws_events_df", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"green_building_materials\").where(\"dt = current_date()\").writeTo(\"countries\").append()\n", "labels": {"reads": [{"table": "green_building_materials", "columns": null}], "writes": [{"table": "countries", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model tweets depends on dws.dws_member_point_di\ndbt run --models tweets --vars '{\"source_table\":\"dws.dws_member_point_di\"}'\n", "labels": {"reads": [{"table": "dws.dws_member_point_di", "columns": null}], "writes": [{"table": "tweets", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO cotton_source SELECT rebounds, inclusive_housing_policy, country_id FROM geologicalsurvey WHERE rebounds > 230\")\n", "labels": {"reads": [{"table": "geologicalsurvey", "columns": ["rebounds", "inclusive_housing_policy", "country_id"]}], "writes": [{"table": "cotton_source", "columns": ["rebounds", "inclusive_housing_policy", "country_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM unesco_intangible_heritage\"\n", "labels": {"reads": [{"table": "unesco_intangible_heritage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO professionals (arrival, booking_start_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "professionals", "columns": ["arrival", "booking_start_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO volunteer_signups SELECT sighting_id, date_valid_from FROM bi.device_log WHERE sighting_id > 192\")\n", "labels": {"reads": [{"table": "bi.device_log", "columns": ["sighting_id", "date_valid_from"]}], "writes": [{"table": "volunteer_signups", "columns": ["sighting_id", "date_valid_from"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO waterconservationbudget SELECT a.artifactname, b.relationship FROM perpetrator a JOIN workforcediversity b ON a.brand = b.brand\"\n", "labels": {"reads": [{"table": "perpetrator", "columns": null}, {"table": "workforcediversity", "columns": null}], "writes": [{"table": "waterconservationbudget", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT accident_date, training_id FROM cerium_production LIMIT 130\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "cerium_production", "columns": ["accident_date", "training_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO artifact_analysis SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"experts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tourism_centers\")\n", "labels": {"reads": [{"table": "experts", "columns": null}], "writes": [{"table": "tourism_centers", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM budget_allocations\"\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 301;\nEOF\n", "labels": {"reads": [{"table": "hr.employees", "columns": ["virtual_tour_engagement_time", "episode_number", "ratingdate", "recipient_id"]}], "writes": [{"table": "film_market_estimation", "columns": ["virtual_tour_engagement_time", "episode_number", "ratingdate", "recipient_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"advocacy\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "advocacy", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_coupon_use_di\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"farmer_details\")\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_di", "columns": null}], "writes": [{"table": "farmer_details", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 339;\nEOF\n", "labels": {"reads": [{"table": "purchase", "columns": ["spf_level", "experience", "issue"]}], "writes": [{"table": "movies", "columns": ["spf_level", "experience", "issue"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.campaigns_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"animals\")\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": null}], "writes": [{"table": "animals", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ticketsales\", conn)\ndf.to_sql(\"ods.campaigns_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ticketsales", "columns": null}], "writes": [{"table": "ods.campaigns_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM water_usage\"\n", "labels": {"reads": [{"table": "water_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO shrimp_farms SELECT dissolved_oxygen, regional_population, directed_by FROM recovery_program WHERE dissolved_oxygen > 173\"\n", "labels": {"reads": [{"table": "recovery_program", "columns": ["dissolved_oxygen", "regional_population", "directed_by"]}], "writes": [{"table": "shrimp_farms", "columns": ["dissolved_oxygen", "regional_population", "directed_by"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT programarea, innovation_id FROM soccer_goals LIMIT 469\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "soccer_goals", "columns": ["programarea", "innovation_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO sustainability SELECT dysprosium_prod, review_rating, school_name FROM healthcare_budget WHERE dysprosium_prod > 243\")\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": ["dysprosium_prod", "review_rating", "school_name"]}], "writes": [{"table": "sustainability", "columns": ["dysprosium_prod", "review_rating", "school_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO show SELECT dishid, college_location FROM distributors WHERE dishid > 154\"], check=True)\n", "labels": {"reads": [{"table": "distributors", "columns": ["dishid", "college_location"]}], "writes": [{"table": "show", "columns": ["dishid", "college_location"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO labor_unions SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"orders\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"volunteerprograms\")\n", "labels": {"reads": [{"table": "orders", "columns": null}], "writes": [{"table": "volunteerprograms", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 244;\nSQL\n", "labels": {"reads": [{"table": "greenbuildings", "columns": ["sponsor_name", "vulnerability"]}, {"table": "sales", "columns": ["event_id", "cloud_cover", "temporary_acting"]}], "writes": [{"table": "students_lifelong_learning", "columns": ["event_id", "cloud_cover", "temporary_acting"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table railway --columns farmer_id,research_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "railway", "columns": ["farmer_id", "research_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ratings SELECT spill_name, intervention_type, advocate_id, writer FROM music_festival WHERE spill_name > 180\"\n", "labels": {"reads": [{"table": "music_festival", "columns": ["spill_name", "intervention_type", "advocate_id", "writer"]}], "writes": [{"table": "ratings", "columns": ["spill_name", "intervention_type", "advocate_id", "writer"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO people SELECT sector_id, route_name, event_id FROM satellitedata WHERE sector_id > 158\")\n", "labels": {"reads": [{"table": "satellitedata", "columns": ["sector_id", "route_name", "event_id"]}], "writes": [{"table": "people", "columns": ["sector_id", "route_name", "event_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nsql = \"INSERT INTO safety_testing SELECT a.provider, b.invoice_details FROM mentalhealthproviders a JOIN mart.mart_products_hourly b ON a.engagement_date = b.engagement_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": null}, {"table": "mart.mart_products_hourly", "columns": null}], "writes": [{"table": "safety_testing", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO prepaid_mobile SELECT development_type, pname, purchase_transaction_id, productname FROM volunteer_signups WHERE development_type > 91\"\n", "labels": {"reads": [{"table": "volunteer_signups", "columns": ["development_type", "pname", "purchase_transaction_id", "productname"]}], "writes": [{"table": "prepaid_mobile", "columns": ["development_type", "pname", "purchase_transaction_id", "productname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"courts\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "courts", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dwd.dwd_payments_full SELECT a.maxoccupancy, b.winning_pilot FROM dws.dws_member_point_df a JOIN fans_merchandise_basketball b ON a.chemical = b.chemical\"\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": null}, {"table": "fans_merchandise_basketball", "columns": null}], "writes": [{"table": "dwd.dwd_payments_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO state_contracts SELECT * FROM legacy\nspark.sql(\"INSERT INTO green_buildings SELECT neighborhood, chw_id, address, exhibitionname FROM chargingstations WHERE neighborhood > 199\")\n", "labels": {"reads": [{"table": "chargingstations", "columns": ["neighborhood", "chw_id", "address", "exhibitionname"]}], "writes": [{"table": "green_buildings", "columns": ["neighborhood", "chw_id", "address", "exhibitionname"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_clicks_di\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"eco_materials\")\n", "labels": {"reads": [{"table": "dws_clicks_di", "columns": null}], "writes": [{"table": "eco_materials", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO mental_health_parity_violations SELECT crop_name, inventory_id FROM tv_shows_genre WHERE crop_name > 347\"\n", "labels": {"reads": [{"table": "tv_shows_genre", "columns": ["crop_name", "inventory_id"]}], "writes": [{"table": "mental_health_parity_violations", "columns": ["crop_name", "inventory_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"menu\")\ndump_to_store(df, \"stg.campaigns_df\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "menu", "columns": null}], "writes": [{"table": "stg.campaigns_df", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO excavation SELECT winning_pilot, publicationid, total_attendance FROM shoes WHERE winning_pilot > 309\")\n", "labels": {"reads": [{"table": "shoes", "columns": ["winning_pilot", "publicationid", "total_attendance"]}], "writes": [{"table": "excavation", "columns": ["winning_pilot", "publicationid", "total_attendance"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dispensaries SELECT transaction_date, call_time, sample_date FROM hospital_equipment WHERE transaction_date > 184\"\n", "labels": {"reads": [{"table": "hospital_equipment", "columns": ["transaction_date", "call_time", "sample_date"]}], "writes": [{"table": "dispensaries", "columns": ["transaction_date", "call_time", "sample_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO manufacturermaterials SELECT points_per_game, billing FROM hospitallocations WHERE points_per_game > 416\")\n", "labels": {"reads": [{"table": "hospitallocations", "columns": ["points_per_game", "billing"]}], "writes": [{"table": "manufacturermaterials", "columns": ["points_per_game", "billing"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ai_for_social_good\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"textile_waste\")\n", "labels": {"reads": [{"table": "ai_for_social_good", "columns": null}], "writes": [{"table": "textile_waste", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO dwd.dwd_users_hourly SELECT workout_duration, model_name FROM fossil_fuel_vehicles WHERE workout_duration > 476\");\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": ["workout_duration", "model_name"]}], "writes": [{"table": "dwd.dwd_users_hourly", "columns": ["workout_duration", "model_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"show\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bank\")\n", "labels": {"reads": [{"table": "show", "columns": null}], "writes": [{"table": "bank", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table shariahfinance --target-dir /tmp/land\n", "labels": {"reads": [{"table": "shariahfinance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM performances\"\n", "labels": {"reads": [{"table": "performances", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO crime_incidents SELECT biomass, donation_date, vehicle_flight_number FROM community_education_programs WHERE biomass > 286\");\n", "labels": {"reads": [{"table": "community_education_programs", "columns": ["biomass", "donation_date", "vehicle_flight_number"]}], "writes": [{"table": "crime_incidents", "columns": ["biomass", "donation_date", "vehicle_flight_number"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO traditional_arts SELECT a.playername, b.market_rate FROM broadband_customers_global a JOIN purchases b ON a.policyid = b.policyid\"\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": null}, {"table": "purchases", "columns": null}], "writes": [{"table": "traditional_arts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hospitals\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"traveler\")\n", "labels": {"reads": [{"table": "hospitals", "columns": null}], "writes": [{"table": "traveler", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO stg_orders_hourly SELECT * FROM legacy\nspark.sql(\"INSERT INTO project_timelines SELECT sales_billion, artwork_id FROM regulatoryframeworksbycountry WHERE sales_billion > 276\")\n", "labels": {"reads": [{"table": "regulatoryframeworksbycountry", "columns": ["sales_billion", "artwork_id"]}], "writes": [{"table": "project_timelines", "columns": ["sales_billion", "artwork_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model stations depends on albums\ndbt build --select stations --vars 'source: albums'\n", "labels": {"reads": [{"table": "albums", "columns": null}], "writes": [{"table": "stations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_inventory_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"testtypes\")\n", "labels": {"reads": [{"table": "bi_inventory_hourly", "columns": null}], "writes": [{"table": "testtypes", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 52;\nSQL\n", "labels": {"reads": [{"table": "premises", "columns": ["transaction_amount", "sale_value"]}, {"table": "neighborhoods", "columns": ["f_id", "launch_date", "vessel_id"]}], "writes": [{"table": "medals", "columns": ["f_id", "launch_date", "vessel_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO assessment_notes SELECT * FROM legacy\nspark.sql(\"INSERT INTO mart.mart_refunds_di SELECT transaction_date, property_id FROM ods.ods_clicks_di WHERE transaction_date > 447\")\n", "labels": {"reads": [{"table": "ods.ods_clicks_di", "columns": ["transaction_date", "property_id"]}], "writes": [{"table": "mart.mart_refunds_di", "columns": ["transaction_date", "property_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT datetime_detention_start, moisture_level FROM stg.stg_users_di\", engine)\nimport logging\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"public.trips_by_day_train\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stg.stg_users_di", "columns": ["datetime_detention_start", "moisture_level"]}], "writes": [{"table": "public.trips_by_day_train", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO trip SELECT store_id, concert_id, updated_at, staff_name FROM reviews WHERE store_id > 478\")\n", "labels": {"reads": [{"table": "reviews", "columns": ["store_id", "concert_id", "updated_at", "staff_name"]}], "writes": [{"table": "trip", "columns": ["store_id", "concert_id", "updated_at", "staff_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"spending\").where(\"dt = current_date()\").writeTo(\"bi.bi_events_daily\").append()\n", "labels": {"reads": [{"table": "spending", "columns": null}], "writes": [{"table": "bi.bi_events_daily", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"members\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"investments_esg\")\n", "labels": {"reads": [{"table": "members", "columns": null}], "writes": [{"table": "investments_esg", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"marathons\").where(\"dt = current_date()\").writeTo(\"florida_conservation_initiatives\").append()\n", "labels": {"reads": [{"table": "marathons", "columns": null}], "writes": [{"table": "florida_conservation_initiatives", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"airlines\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"characteristics\")\n", "labels": {"reads": [{"table": "airlines", "columns": null}], "writes": [{"table": "characteristics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO animal_population SELECT a.signup_date, b.excavation_site_id FROM login_attempts a JOIN fertilizer_usage b ON a.emp_fname = b.emp_fname\"\n", "labels": {"reads": [{"table": "login_attempts", "columns": null}, {"table": "fertilizer_usage", "columns": null}], "writes": [{"table": "animal_population", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO satellitedata SELECT a.omim, b.installed_date FROM sustainable_practices_2 a JOIN representative b ON a.dlocation = b.dlocation\"\n", "labels": {"reads": [{"table": "sustainable_practices_2", "columns": null}, {"table": "representative", "columns": null}], "writes": [{"table": "satellitedata", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.mart_risk_score_hourly SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"chip_model\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "chip_model", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM community_policing\"\n", "labels": {"reads": [{"table": "community_policing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 146;\nEOF\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": ["custid", "roomid", "warehouse_name", "milestone"]}], "writes": [{"table": "workforce_development", "columns": ["custid", "roomid", "warehouse_name", "milestone"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO local_impact_japan SELECT operating_system, initiative_id, focal_length_mm, artwork FROM wastewatertreatment WHERE operating_system > 360\"\n", "labels": {"reads": [{"table": "wastewatertreatment", "columns": ["operating_system", "initiative_id", "focal_length_mm", "artwork"]}], "writes": [{"table": "local_impact_japan", "columns": ["operating_system", "initiative_id", "focal_length_mm", "artwork"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO solana_transactions (principal_activities, player_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "solana_transactions", "columns": ["principal_activities", "player_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT posted_at, online_dispute_resolution FROM climate_finance_re LIMIT 499\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO round SELECT issues, firstname, document_structure_description FROM equipment_sales WHERE issues > 334\")\n", "labels": {"reads": [{"table": "climate_finance_re", "columns": ["posted_at", "online_dispute_resolution"]}, {"table": "equipment_sales", "columns": ["issues", "firstname", "document_structure_description"]}], "writes": [{"table": "round", "columns": ["issues", "firstname", "document_structure_description"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"shipmentinfo\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bus_fares\")\n", "labels": {"reads": [{"table": "shipmentinfo", "columns": null}], "writes": [{"table": "bus_fares", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.orders\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ads.orders", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table research_vessels --target-dir /tmp/land\n", "labels": {"reads": [{"table": "research_vessels", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_policing\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"continents\")\n", "labels": {"reads": [{"table": "community_policing", "columns": null}], "writes": [{"table": "continents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 247;\nSQL\n", "labels": {"reads": [{"table": "biosensors.readings", "columns": ["ship_date", "machine_series"]}, {"table": "ref_budget_codes", "columns": ["consumption", "date_of_latest_logon"]}], "writes": [{"table": "dw.dw_member_point_hourly", "columns": ["consumption", "date_of_latest_logon"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO investors SELECT bookings, quantitysold, visitorid FROM apac_hotel_views WHERE bookings > 401\")\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": ["bookings", "quantitysold", "visitorid"]}], "writes": [{"table": "investors", "columns": ["bookings", "quantitysold", "visitorid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO auctions SELECT billid, attorney_id, taxi_model FROM pollutionincidents WHERE billid > 191\"\n", "labels": {"reads": [{"table": "pollutionincidents", "columns": ["billid", "attorney_id", "taxi_model"]}], "writes": [{"table": "auctions", "columns": ["billid", "attorney_id", "taxi_model"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods_risk_score_delta\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"vessel_tracking\")\n", "labels": {"reads": [{"table": "ods_risk_score_delta", "columns": null}], "writes": [{"table": "vessel_tracking", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"domesticconferences\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "domesticconferences", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"traveler\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "traveler", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO atlantic_ocean_fish SELECT a.num_workers, b.manager_id FROM timber_sales a JOIN africa_projects b ON a.category_name = b.category_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "timber_sales", "columns": null}, {"table": "africa_projects", "columns": null}], "writes": [{"table": "atlantic_ocean_fish", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO waste SELECT make, total_horses, tree_species FROM dysprosiumproduction WHERE make > 417\"], check=True)\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": ["make", "total_horses", "tree_species"]}], "writes": [{"table": "waste", "columns": ["make", "total_horses", "tree_species"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table whale_sightings --columns fan_age,status_of_thing_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "whale_sightings", "columns": ["fan_age", "status_of_thing_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO new_schedules SELECT individual_id, membership_id, veteran_id, route FROM explainable_ai WHERE individual_id > 374\");\n", "labels": {"reads": [{"table": "explainable_ai", "columns": ["individual_id", "membership_id", "veteran_id", "route"]}], "writes": [{"table": "new_schedules", "columns": ["individual_id", "membership_id", "veteran_id", "route"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM grant\"\n", "labels": {"reads": [{"table": "grant", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 88;\nSQL\n", "labels": {"reads": [{"table": "restaurants", "columns": ["calendar_date", "fundingamount"]}, {"table": "ocean_acidification_antarctic", "columns": ["date_to", "spacecraftid", "vendor_name"]}], "writes": [{"table": "investment", "columns": ["date_to", "spacecraftid", "vendor_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mental_health_clinics\");\ndf.write().mode(\"overwrite\").saveAsTable(\"bi.inventory_delta\");\n", "labels": {"reads": [{"table": "mental_health_clinics", "columns": null}], "writes": [{"table": "bi.inventory_delta", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM accessibility_audits\"\n", "labels": {"reads": [{"table": "accessibility_audits", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO mart.mart_vendors SELECT advisoryid, reason, max_page_size, time_of_purchase FROM producersnewmexico WHERE advisoryid > 434\"\n", "labels": {"reads": [{"table": "producersnewmexico", "columns": ["advisoryid", "reason", "max_page_size", "time_of_purchase"]}], "writes": [{"table": "mart.mart_vendors", "columns": ["advisoryid", "reason", "max_page_size", "time_of_purchase"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"waterconservationbudget\").toPandas()\ndf[[\"visitor_country\", \"plantlocation\"]].to_sql(\"ads_cart_item_hourly\", engine, index=False)\n", "labels": {"reads": [{"table": "waterconservationbudget", "columns": null}], "writes": [{"table": "ads_cart_item_hourly", "columns": ["visitor_country", "plantlocation"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO tourdifferences SELECT moisture, session_name FROM space_exploration WHERE moisture > 321\")\n", "labels": {"reads": [{"table": "space_exploration", "columns": ["moisture", "session_name"]}], "writes": [{"table": "tourdifferences", "columns": ["moisture", "session_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO flight_emissions SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT incorporated_in, bias_score FROM cyber_incidents LIMIT 5\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "cyber_incidents", "columns": ["incorporated_in", "bias_score"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wells SELECT * FROM legacy\ncur.execute(\"SELECT subject_id, award FROM readership LIMIT 410\")\n", "labels": {"reads": [{"table": "readership", "columns": ["subject_id", "award"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 183;\nSQL\n", "labels": {"reads": [{"table": "territory.human_rights_data", "columns": ["crop_id", "developer"]}, {"table": "consumer", "columns": ["document_structure_description", "participatedinesports", "hub_id", "objectnumber"]}], "writes": [{"table": "incidents_by_month", "columns": ["document_structure_description", "participatedinesports", "hub_id", "objectnumber"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT eco_friendly, meter_200 FROM african_tourism LIMIT 129\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO manufacturingplants SELECT journalist_id, host_country, vegan, sale_year FROM candidate_assessments WHERE journalist_id > 196\")\n", "labels": {"reads": [{"table": "african_tourism", "columns": ["eco_friendly", "meter_200"]}, {"table": "candidate_assessments", "columns": ["journalist_id", "host_country", "vegan", "sale_year"]}], "writes": [{"table": "manufacturingplants", "columns": ["journalist_id", "host_country", "vegan", "sale_year"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO student_access SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO defense_project_timelines SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ethical_ai\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ethical_ai", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO casesbyyear SELECT contributor, itemid, dept_id FROM tb_reports WHERE contributor > 455\"\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["contributor", "itemid", "dept_id"]}], "writes": [{"table": "casesbyyear", "columns": ["contributor", "itemid", "dept_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO tree_types SELECT director, neighborhood FROM mars_spacecraft WHERE director > 33\")\n", "labels": {"reads": [{"table": "mars_spacecraft", "columns": ["director", "neighborhood"]}], "writes": [{"table": "tree_types", "columns": ["director", "neighborhood"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ca_menu_items SELECT a.task_id, b.session_language FROM ads_payments_di a JOIN mart.mart_device_log_delta b ON a.production_date = b.production_date\"\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": null}, {"table": "mart.mart_device_log_delta", "columns": null}], "writes": [{"table": "ca_menu_items", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table aus_wellbeing --columns cell_mobile_number,vendorid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "aus_wellbeing", "columns": ["cell_mobile_number", "vendorid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"sports_events\").where(\"dt = current_date()\").writeTo(\"emergency_categories\").append()\n", "labels": {"reads": [{"table": "sports_events", "columns": null}], "writes": [{"table": "emergency_categories", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"follows\");\ndf.write().mode(\"overwrite\").saveAsTable(\"artist\");\n", "labels": {"reads": [{"table": "follows", "columns": null}], "writes": [{"table": "artist", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table military_aircraft_maintenance --target-dir /tmp/land\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"freshwaterfinfish\")\ndump_to_output(df, \"galleryc\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "freshwaterfinfish", "columns": null}], "writes": [{"table": "galleryc", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"aircraft\").toPandas()\ndf[[\"company_id\", \"safety_score\"]].to_sql(\"factories_africa\", engine, index=False)\n", "labels": {"reads": [{"table": "aircraft", "columns": null}], "writes": [{"table": "factories_africa", "columns": ["company_id", "safety_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.capacity_mw > 71).all()\n# src table: salinity_readings\nengine.execute(\"INSERT INTO daily_articles_by_category SELECT * FROM salinity_readings\")\n", "labels": {"reads": [{"table": "salinity_readings", "columns": null}], "writes": [{"table": "daily_articles_by_category", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO emergency_categories SELECT network, warehousename FROM sfc_articles WHERE network > 454\"\n", "labels": {"reads": [{"table": "sfc_articles", "columns": ["network", "warehousename"]}], "writes": [{"table": "emergency_categories", "columns": ["network", "warehousename"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO cybersecurity_incidents SELECT gradepoint, org_id FROM stg.stg_risk_score WHERE gradepoint > 421\");\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": ["gradepoint", "org_id"]}], "writes": [{"table": "cybersecurity_incidents", "columns": ["gradepoint", "org_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fairtradefactories SELECT enable_location_tracking, model, capacity_mw FROM request WHERE enable_location_tracking > 58\"], check=True)\n", "labels": {"reads": [{"table": "request", "columns": ["enable_location_tracking", "model", "capacity_mw"]}], "writes": [{"table": "fairtradefactories", "columns": ["enable_location_tracking", "model", "capacity_mw"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"miningoperations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "miningoperations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM operate_company\"\n", "labels": {"reads": [{"table": "operate_company", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO transport SELECT coach_name, statement_id FROM electric_buses WHERE coach_name > 225\"\n", "labels": {"reads": [{"table": "electric_buses", "columns": ["coach_name", "statement_id"]}], "writes": [{"table": "transport", "columns": ["coach_name", "statement_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"user_likes\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "user_likes", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO constructionlaborstatistics (affirmative, time_day) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "constructionlaborstatistics", "columns": ["affirmative", "time_day"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table shared_escooters --target-dir /tmp/land\n", "labels": {"reads": [{"table": "shared_escooters", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO mine SELECT sale_date, prominence, stat_id FROM nyc_subway WHERE sale_date > 243\");\n", "labels": {"reads": [{"table": "nyc_subway", "columns": ["sale_date", "prominence", "stat_id"]}], "writes": [{"table": "mine", "columns": ["sale_date", "prominence", "stat_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"infra_diversification\").where(\"dt = current_date()\").writeTo(\"clothing_brands\").append()\n", "labels": {"reads": [{"table": "infra_diversification", "columns": null}], "writes": [{"table": "clothing_brands", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO healthequitymetrics SELECT * FROM legacy\nspark.sql(\"INSERT INTO wastewater_treatment SELECT clicks, waste_id, active_to_date FROM ods_risk_score_delta WHERE clicks > 269\")\n", "labels": {"reads": [{"table": "ods_risk_score_delta", "columns": ["clicks", "waste_id", "active_to_date"]}], "writes": [{"table": "wastewater_treatment", "columns": ["clicks", "waste_id", "active_to_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO menu_items SELECT * FROM legacy\nspark.sql(\"INSERT INTO business_rates SELECT claim_type, bookings FROM textileworkers WHERE claim_type > 118\")\n", "labels": {"reads": [{"table": "textileworkers", "columns": ["claim_type", "bookings"]}], "writes": [{"table": "business_rates", "columns": ["claim_type", "bookings"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO rating SELECT functional_area_code, playlist_id, dock_status, donator_name FROM environmentalimpact WHERE functional_area_code > 142\"], check=True)\n", "labels": {"reads": [{"table": "environmentalimpact", "columns": ["functional_area_code", "playlist_id", "dock_status", "donator_name"]}], "writes": [{"table": "rating", "columns": ["functional_area_code", "playlist_id", "dock_status", "donator_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table jupiter_missions --columns company_type_code,passenger_count --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "jupiter_missions", "columns": ["company_type_code", "passenger_count"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"movie\");\ndf.write().mode(\"overwrite\").saveAsTable(\"student_program_mapping\");\n", "labels": {"reads": [{"table": "movie", "columns": null}], "writes": [{"table": "student_program_mapping", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO traveler SELECT subject, emp_dob, drugname FROM dwd.dwd_inventory_hourly WHERE subject > 89\")\n", "labels": {"reads": [{"table": "dwd.dwd_inventory_hourly", "columns": ["subject", "emp_dob", "drugname"]}], "writes": [{"table": "traveler", "columns": ["subject", "emp_dob", "drugname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cargos SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthprovider\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"smartcities\")\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": null}], "writes": [{"table": "smartcities", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO market SELECT cmi_cross_ref_id, number_deaths, violation_id FROM stateinfrastructure WHERE cmi_cross_ref_id > 283\"\n", "labels": {"reads": [{"table": "stateinfrastructure", "columns": ["cmi_cross_ref_id", "number_deaths", "violation_id"]}], "writes": [{"table": "market", "columns": ["cmi_cross_ref_id", "number_deaths", "violation_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.bi_sessions_df (orgname, apt_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_sessions_df", "columns": ["orgname", "apt_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.mediatorid > 418).all()\n# src table: ods_products_delta\nengine.execute(\"INSERT INTO animal_rehab SELECT * FROM ods_products_delta\")\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": null}], "writes": [{"table": "animal_rehab", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO whale_sightings SELECT * FROM legacy\nspark.sql(\"INSERT INTO adaptation_projects SELECT patient_id, case_type, distributorid, pieces FROM ai_for_social_good WHERE patient_id > 441\")\n", "labels": {"reads": [{"table": "ai_for_social_good", "columns": ["patient_id", "case_type", "distributorid", "pieces"]}], "writes": [{"table": "adaptation_projects", "columns": ["patient_id", "case_type", "distributorid", "pieces"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO greenbuildings SELECT a.city_id, b.exit_date FROM bi.risk_score_df a JOIN restaurant_type b ON a.bill_id = b.bill_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi.risk_score_df", "columns": null}, {"table": "restaurant_type", "columns": null}], "writes": [{"table": "greenbuildings", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"artifact_analysis\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"regular_order_products\")\nsrc.write.insertInto(\"reo_production\", overwrite=True)\n", "labels": {"reads": [{"table": "regular_order_products", "columns": null}], "writes": [{"table": "reo_production", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO carbon_footprint SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 56;\nSQL\n", "labels": {"reads": [{"table": "auctions", "columns": ["time_second", "phone"]}, {"table": "brazil_projects", "columns": ["route_short_name", "ironquantity"]}], "writes": [{"table": "dws.dws_users_hourly", "columns": ["route_short_name", "ironquantity"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 425;\nEOF\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": ["quantity_containers", "pilot"]}], "writes": [{"table": "dws.events", "columns": ["quantity_containers", "pilot"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO mobile_usage SELECT * FROM legacy\nspark.sql(\"INSERT INTO demographics SELECT score, staff_id, attraction_name FROM hotel_business_partnerships WHERE score > 486\")\n", "labels": {"reads": [{"table": "hotel_business_partnerships", "columns": ["score", "staff_id", "attraction_name"]}], "writes": [{"table": "demographics", "columns": ["score", "staff_id", "attraction_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO infection_rates SELECT strategy, visitid, founder_group, ship_name FROM engineer_visits WHERE strategy > 45\"\n", "labels": {"reads": [{"table": "engineer_visits", "columns": ["strategy", "visitid", "founder_group", "ship_name"]}], "writes": [{"table": "infection_rates", "columns": ["strategy", "visitid", "founder_group", "ship_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT num_volunteers, spacecraft_id FROM militarypersonnel LIMIT 29\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": ["num_volunteers", "spacecraft_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO satellitedata SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO mart.shipments_full SELECT prof_num, loan_id FROM healthcare_centers WHERE prof_num > 494\"\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": ["prof_num", "loan_id"]}], "writes": [{"table": "mart.shipments_full", "columns": ["prof_num", "loan_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO studentaccommodations SELECT a.startyear, b.updated_at FROM tech_volunteers a JOIN ads.ads_payments_hourly b ON a.date_joined_staff = b.date_joined_staff\"\n", "labels": {"reads": [{"table": "tech_volunteers", "columns": null}, {"table": "ads.ads_payments_hourly", "columns": null}], "writes": [{"table": "studentaccommodations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO public_transport.passenger_count SELECT * FROM legacy\nspark.sql(\"INSERT INTO vessel SELECT sighting_date, visitorid FROM mentalhealthprofessional WHERE sighting_date > 245\")\n", "labels": {"reads": [{"table": "mentalhealthprofessional", "columns": ["sighting_date", "visitorid"]}], "writes": [{"table": "vessel", "columns": ["sighting_date", "visitorid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO pitstops SELECT developer_id, wellbeing_score FROM advisor WHERE developer_id > 425\"\n", "labels": {"reads": [{"table": "advisor", "columns": ["developer_id", "wellbeing_score"]}], "writes": [{"table": "pitstops", "columns": ["developer_id", "wellbeing_score"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT attraction_type_code, releasedate FROM music LIMIT 431\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "music", "columns": ["attraction_type_code", "releasedate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_input(ctx, \"uel_top10\")\nwrite_to_target(df, \"product_ingredient\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "uel_top10", "columns": null}], "writes": [{"table": "product_ingredient", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table pets --columns production,booking_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "pets", "columns": ["production", "booking_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO team_members SELECT horizontal_bar_points, annual_revenue, animal_id, num_developments FROM directors WHERE horizontal_bar_points > 359\");\n", "labels": {"reads": [{"table": "directors", "columns": ["horizontal_bar_points", "annual_revenue", "animal_id", "num_developments"]}], "writes": [{"table": "team_members", "columns": ["horizontal_bar_points", "annual_revenue", "animal_id", "num_developments"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 462;\nEOF\n", "labels": {"reads": [{"table": "stay", "columns": ["fault_description", "stop"]}], "writes": [{"table": "recyclers", "columns": ["fault_description", "stop"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT all_games, mean_temperature_f FROM measurements\", engine)\nimport logging\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"audience_demographics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "measurements", "columns": ["all_games", "mean_temperature_f"]}], "writes": [{"table": "audience_demographics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dailyapplestreams\").where(\"dt = current_date()\").writeTo(\"rainfall_data\").append()\n", "labels": {"reads": [{"table": "dailyapplestreams", "columns": null}], "writes": [{"table": "rainfall_data", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dw.inventory_delta\", conn)\ndf.to_sql(\"mart.shipments_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": null}], "writes": [{"table": "mart.shipments_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table city_properties --columns crop_name,meal_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "city_properties", "columns": ["crop_name", "meal_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.spacecraft_id > 6).all()\n# src table: production\nengine.execute(\"INSERT INTO mart_exposure_hourly SELECT * FROM production\")\n", "labels": {"reads": [{"table": "production", "columns": null}], "writes": [{"table": "mart_exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO legal_aid_providers SELECT * FROM legacy\ncur.execute(\"SELECT virtual_tour_views, warehouseid FROM housing_investments LIMIT 492\")\n", "labels": {"reads": [{"table": "housing_investments", "columns": ["virtual_tour_views", "warehouseid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO elimination SELECT total_horses, route_id FROM sustainableproduction WHERE total_horses > 73\")\n", "labels": {"reads": [{"table": "sustainableproduction", "columns": ["total_horses", "route_id"]}], "writes": [{"table": "elimination", "columns": ["total_horses", "route_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO cybersecurity_vulnerabilities SELECT constructorid, container_id FROM wastedata WHERE constructorid > 315\")\n", "labels": {"reads": [{"table": "wastedata", "columns": ["constructorid", "container_id"]}], "writes": [{"table": "cybersecurity_vulnerabilities", "columns": ["constructorid", "container_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO habitat_preservation SELECT * FROM legacy\nspark.sql(\"INSERT INTO bi.bi_events_full SELECT brandname, service_id, q1_2022_views FROM maintenance_requests WHERE brandname > 219\")\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": ["brandname", "service_id", "q1_2022_views"]}], "writes": [{"table": "bi.bi_events_full", "columns": ["brandname", "service_id", "q1_2022_views"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mission SELECT a.fare_date, b.low_estimate FROM vehicle_sales a JOIN labour_productivity b ON a.reaction_time = b.reaction_time\"\n", "labels": {"reads": [{"table": "vehicle_sales", "columns": null}, {"table": "labour_productivity", "columns": null}], "writes": [{"table": "mission", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT launched_year, unique_founders FROM cities LIMIT 447\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO dwd_risk_score_hourly SELECT tourists, claim_outcome_code, clublocation FROM wastegeneration WHERE tourists > 34\")\n", "labels": {"reads": [{"table": "cities", "columns": ["launched_year", "unique_founders"]}, {"table": "wastegeneration", "columns": ["tourists", "claim_outcome_code", "clublocation"]}], "writes": [{"table": "dwd_risk_score_hourly", "columns": ["tourists", "claim_outcome_code", "clublocation"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT constructorid, temporary_acting FROM threat_severity\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"safety_violations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "threat_severity", "columns": ["constructorid", "temporary_acting"]}], "writes": [{"table": "safety_violations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"warehouses\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"stg.risk_score_hourly\")\n", "labels": {"reads": [{"table": "warehouses", "columns": null}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"fare_segments\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM cuisine\", conn)\ndf.to_sql(\"bi.bi_risk_score_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "cuisine", "columns": null}], "writes": [{"table": "bi.bi_risk_score_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT transaction_amount, personnelbranch FROM ingredientsvegancrueltyfree\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"engineer_skills\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ingredientsvegancrueltyfree", "columns": ["transaction_amount", "personnelbranch"]}], "writes": [{"table": "engineer_skills", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO smart_city_projects SELECT team_id_br, reign, sustainability_id, product_category_code FROM aquaticfarm WHERE team_id_br > 455\");\n", "labels": {"reads": [{"table": "aquaticfarm", "columns": ["team_id_br", "reign", "sustainability_id", "product_category_code"]}], "writes": [{"table": "smart_city_projects", "columns": ["team_id_br", "reign", "sustainability_id", "product_category_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.dws_risk_score_daily SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.annual_revenue > 204).all()\n# src table: clothing_brands\nengine.execute(\"INSERT INTO material SELECT * FROM clothing_brands\")\n", "labels": {"reads": [{"table": "clothing_brands", "columns": null}], "writes": [{"table": "material", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"storage_projects\").toPandas()\ndf[[\"zone\", \"dob\"]].to_sql(\"bi.refunds_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "storage_projects", "columns": null}], "writes": [{"table": "bi.refunds_daily", "columns": ["zone", "dob"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO emergency_calls SELECT crs_credit, issue_id, observation_id, birthdate FROM ads.ads_payments_delta WHERE crs_credit > 492\")\n", "labels": {"reads": [{"table": "ads.ads_payments_delta", "columns": ["crs_credit", "issue_id", "observation_id", "birthdate"]}], "writes": [{"table": "emergency_calls", "columns": ["crs_credit", "issue_id", "observation_id", "birthdate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table gene --target-dir /tmp/land\n", "labels": {"reads": [{"table": "gene", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO technology_access SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO vessel_performance SELECT arrival_date, altitude FROM textileworkers WHERE arrival_date > 464\"\n", "labels": {"reads": [{"table": "textileworkers", "columns": ["arrival_date", "altitude"]}], "writes": [{"table": "vessel_performance", "columns": ["arrival_date", "altitude"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO cosmetics_sales SELECT customer_address, trip_duration, doctorsper1000, financial_capability_score FROM salary WHERE customer_address > 389\"\n", "labels": {"reads": [{"table": "salary", "columns": ["customer_address", "trip_duration", "doctorsper1000", "financial_capability_score"]}], "writes": [{"table": "cosmetics_sales", "columns": ["customer_address", "trip_duration", "doctorsper1000", "financial_capability_score"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO vehicle_registrations SELECT task, individual_middle_name, start, inspection_id FROM payments WHERE task > 390\");\n", "labels": {"reads": [{"table": "payments", "columns": ["task", "individual_middle_name", "start", "inspection_id"]}], "writes": [{"table": "vehicle_registrations", "columns": ["task", "individual_middle_name", "start", "inspection_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO recycling_rates_oceania SELECT a.emp_lname, b.organizationname FROM ads.orders a JOIN southchinasea.wells b ON a.time_day = b.time_day\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.orders", "columns": null}, {"table": "southchinasea.wells", "columns": null}], "writes": [{"table": "recycling_rates_oceania", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"waste_generation_city_v2\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "waste_generation_city_v2", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 146;\nEOF\n", "labels": {"reads": [{"table": "satellites", "columns": ["vice_president_vote", "shariah_compliant_investment_amount", "other_characteristic_details"]}], "writes": [{"table": "readership", "columns": ["vice_president_vote", "shariah_compliant_investment_amount", "other_characteristic_details"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.therapist_id > 479).all()\n# src table: ads_payments_daily\nengine.execute(\"INSERT INTO ship SELECT * FROM ads_payments_daily\")\n", "labels": {"reads": [{"table": "ads_payments_daily", "columns": null}], "writes": [{"table": "ship", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bi.bi_inventory_full\").where(\"dt = current_date()\").writeTo(\"space_exploration\").append()\n", "labels": {"reads": [{"table": "bi.bi_inventory_full", "columns": null}], "writes": [{"table": "space_exploration", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"mart.mart_users_delta\")\npush_to_output(df, \"product_catalog\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_users_delta", "columns": null}], "writes": [{"table": "product_catalog", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"multimodalhubs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO eu_data_usage SELECT * FROM legacy\nspark.sql(\"INSERT INTO ads.campaigns_di SELECT personnel, launch_company, product_category_code, party_id FROM engineer_skills WHERE personnel > 144\")\n", "labels": {"reads": [{"table": "engineer_skills", "columns": ["personnel", "launch_company", "product_category_code", "party_id"]}], "writes": [{"table": "ads.campaigns_di", "columns": ["personnel", "launch_company", "product_category_code", "party_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table user_reactions --target-dir /tmp/land\n", "labels": {"reads": [{"table": "user_reactions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 390;\nEOF\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": ["company_gender", "sentence_id", "location", "acidity_level"]}], "writes": [{"table": "paris_train", "columns": ["company_gender", "sentence_id", "location", "acidity_level"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"healthcare_budget\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"research_grants\")\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": null}], "writes": [{"table": "research_grants", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM authorship\", conn)\ndf.to_sql(\"waste_management_projects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "authorship", "columns": null}], "writes": [{"table": "waste_management_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ods_products_delta depends on grapes\ndbt run --select ods_products_delta --vars '{\"src\":\"grapes\"}'\n", "labels": {"reads": [{"table": "grapes", "columns": null}], "writes": [{"table": "ods_products_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO communityengagement SELECT funding_id, departmentid FROM miningwaterusage WHERE funding_id > 343\")\n", "labels": {"reads": [{"table": "miningwaterusage", "columns": ["funding_id", "departmentid"]}], "writes": [{"table": "communityengagement", "columns": ["funding_id", "departmentid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_input(ctx, \"wind_energy_projects\")\npush_to_output(df, \"bridge\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": null}], "writes": [{"table": "bridge", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"diversion_programs\");\ndf.write().mode(\"overwrite\").saveAsTable(\"public.forest_stats\");\n", "labels": {"reads": [{"table": "diversion_programs", "columns": null}], "writes": [{"table": "public.forest_stats", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT partitionid, employer_organisation_id FROM healthcare_budget LIMIT 81\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO canada_tech SELECT interest_group, claim_stage_id, deliveryid, founded FROM disaster_mitigation WHERE interest_group > 8\")\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": ["partitionid", "employer_organisation_id"]}, {"table": "disaster_mitigation", "columns": ["interest_group", "claim_stage_id", "deliveryid", "founded"]}], "writes": [{"table": "canada_tech", "columns": ["interest_group", "claim_stage_id", "deliveryid", "founded"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"device_accessibility\");\ndf.write().mode(\"overwrite\").saveAsTable(\"prison\");\n", "labels": {"reads": [{"table": "device_accessibility", "columns": null}], "writes": [{"table": "prison", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO party_host SELECT workforce_development, area_name, name, practice_id FROM mart.mart_payments_df WHERE workforce_development > 125\");\n", "labels": {"reads": [{"table": "mart.mart_payments_df", "columns": ["workforce_development", "area_name", "name", "practice_id"]}], "writes": [{"table": "party_host", "columns": ["workforce_development", "area_name", "name", "practice_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO onlineengagement SELECT partid, trip_date, temp, resource FROM militarypersonnel WHERE partid > 37\")\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": ["partid", "trip_date", "temp", "resource"]}], "writes": [{"table": "onlineengagement", "columns": ["partid", "trip_date", "temp", "resource"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM engineer_visits\", conn)\ndf.to_sql(\"entrepreneur\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "engineer_visits", "columns": null}], "writes": [{"table": "entrepreneur", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT digital_channel, passenger_id FROM lead_mines LIMIT 29\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO biomes SELECT browser_id, participant_details, report_date FROM vessel_registry WHERE browser_id > 2\")\n", "labels": {"reads": [{"table": "lead_mines", "columns": ["digital_channel", "passenger_id"]}, {"table": "vessel_registry", "columns": ["browser_id", "participant_details", "report_date"]}], "writes": [{"table": "biomes", "columns": ["browser_id", "participant_details", "report_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.citizens > 55).all()\n# src table: device_usage\nengine.execute(\"INSERT INTO ods.ods_users_daily SELECT * FROM device_usage\")\n", "labels": {"reads": [{"table": "device_usage", "columns": null}], "writes": [{"table": "ods.ods_users_daily", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_source(ctx, \"carbon_prices_3\")\nsink_to_sink(df, \"demographics\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": null}], "writes": [{"table": "demographics", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO skincareinventory SELECT * FROM legacy\ncur.execute(\"SELECT org_id, offense FROM mentalhealthparityscores LIMIT 152\")\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": ["org_id", "offense"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO constructorstandings SELECT attorneyid, call_id, event_location FROM geologicalsurvey WHERE attorneyid > 404\"], check=True)\n", "labels": {"reads": [{"table": "geologicalsurvey", "columns": ["attorneyid", "call_id", "event_location"]}], "writes": [{"table": "constructorstandings", "columns": ["attorneyid", "call_id", "event_location"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ods.member_point_df SELECT * FROM legacy\nspark.sql(\"INSERT INTO customers SELECT workforce_development, releaseyear, treatment_id, coowner_name FROM rural_economy_2 WHERE workforce_development > 60\")\n", "labels": {"reads": [{"table": "rural_economy_2", "columns": ["workforce_development", "releaseyear", "treatment_id", "coowner_name"]}], "writes": [{"table": "customers", "columns": ["workforce_development", "releaseyear", "treatment_id", "coowner_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO patient_outcomes SELECT hotel_id, directed_by, away_team_id, wage FROM agencies WHERE hotel_id > 120\");\n", "labels": {"reads": [{"table": "agencies", "columns": ["hotel_id", "directed_by", "away_team_id", "wage"]}], "writes": [{"table": "patient_outcomes", "columns": ["hotel_id", "directed_by", "away_team_id", "wage"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO phone SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM unionmembers\"\n", "labels": {"reads": [{"table": "unionmembers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"workforce_development_programs\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO chemicalbatches SELECT * FROM legacy\nspark.sql(\"INSERT INTO tv_shows SELECT shop_name, commodity FROM dwd.dwd_member_point_di WHERE shop_name > 500\")\n", "labels": {"reads": [{"table": "dwd.dwd_member_point_di", "columns": ["shop_name", "commodity"]}], "writes": [{"table": "tv_shows", "columns": ["shop_name", "commodity"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_events_daily SELECT waste_id, inspection_date, mean_sea_level_pressure_inches FROM claims_documents WHERE waste_id > 300\"\n", "labels": {"reads": [{"table": "claims_documents", "columns": ["waste_id", "inspection_date", "mean_sea_level_pressure_inches"]}], "writes": [{"table": "ods.ods_events_daily", "columns": ["waste_id", "inspection_date", "mean_sea_level_pressure_inches"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO tech_workers_union SELECT customer_details, dish_type, flight_number FROM carbon_pricing WHERE customer_details > 255\")\n", "labels": {"reads": [{"table": "carbon_pricing", "columns": ["customer_details", "dish_type", "flight_number"]}], "writes": [{"table": "tech_workers_union", "columns": ["customer_details", "dish_type", "flight_number"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"music\")\nsink_to_warehouse(df, \"scientists\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "music", "columns": null}], "writes": [{"table": "scientists", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ai_projects\", conn)\ndf.to_sql(\"stg.stg_risk_score_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ai_projects", "columns": null}], "writes": [{"table": "stg.stg_risk_score_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.event_type_id > 459).all()\n# src table: stg.events_hourly\nengine.execute(\"INSERT INTO electricvehicleadoption SELECT * FROM stg.events_hourly\")\n", "labels": {"reads": [{"table": "stg.events_hourly", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"carbon_footprint\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT evaluated_for_fairness, service_name FROM co2emissions LIMIT 375\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO disinformation_detection SELECT feature_id, location_description, hotel_name FROM team_members WHERE feature_id > 400\")\n", "labels": {"reads": [{"table": "co2emissions", "columns": ["evaluated_for_fairness", "service_name"]}, {"table": "team_members", "columns": ["feature_id", "location_description", "hotel_name"]}], "writes": [{"table": "disinformation_detection", "columns": ["feature_id", "location_description", "hotel_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO marine_species_status SELECT genre_is, copy_number FROM yttrium_production WHERE genre_is > 418\")\n", "labels": {"reads": [{"table": "yttrium_production", "columns": ["genre_is", "copy_number"]}], "writes": [{"table": "marine_species_status", "columns": ["genre_is", "copy_number"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT quarter, draft_class FROM recall_reports LIMIT 277\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "recall_reports", "columns": ["quarter", "draft_class"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 272;\nEOF\n", "labels": {"reads": [{"table": "programoutcomes", "columns": ["next_entry_id", "followers", "species_id", "tour_id"]}], "writes": [{"table": "postseason", "columns": ["next_entry_id", "followers", "species_id", "tour_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT warehouse_state, fueldate FROM dwd.dwd_payments_di\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ndf.to_sql(\"performers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_payments_di", "columns": ["warehouse_state", "fueldate"]}], "writes": [{"table": "performers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO subjects (home_city, accreditation_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "subjects", "columns": ["home_city", "accreditation_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.stg_campaigns_hourly SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"grapes\");\ndf.write().mode(\"overwrite\").saveAsTable(\"beauty_products\");\n", "labels": {"reads": [{"table": "grapes", "columns": null}], "writes": [{"table": "beauty_products", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"circular_economy_initiatives\").toPandas()\ndf[[\"campaign_name\", \"astronaut_name\"]].to_sql(\"sales_by_quarter\", engine, index=False)\n", "labels": {"reads": [{"table": "circular_economy_initiatives", "columns": null}], "writes": [{"table": "sales_by_quarter", "columns": ["campaign_name", "astronaut_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.stg_coupon_use_di --columns lot_id,ticket_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_coupon_use_di", "columns": ["lot_id", "ticket_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"workforce_training\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "workforce_training", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT is_accessible, citation_time FROM mobile_usage LIMIT 83\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "mobile_usage", "columns": ["is_accessible", "citation_time"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO miningwaterusage SELECT marketing_region_descriptrion, flag FROM production WHERE marketing_region_descriptrion > 316\");\n", "labels": {"reads": [{"table": "production", "columns": ["marketing_region_descriptrion", "flag"]}], "writes": [{"table": "miningwaterusage", "columns": ["marketing_region_descriptrion", "flag"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"recyclingratessouthamerica\").toPandas()\ndf[[\"commission_pct\", \"country\"]].to_sql(\"habitat_preservation\", engine, index=False)\n", "labels": {"reads": [{"table": "recyclingratessouthamerica", "columns": null}], "writes": [{"table": "habitat_preservation", "columns": ["commission_pct", "country"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi.bi_member_point\");\ndf.write().mode(\"overwrite\").saveAsTable(\"refugee_support\");\n", "labels": {"reads": [{"table": "bi.bi_member_point", "columns": null}], "writes": [{"table": "refugee_support", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"expenses\");\ndf.write().mode(\"overwrite\").saveAsTable(\"nomination\");\n", "labels": {"reads": [{"table": "expenses", "columns": null}], "writes": [{"table": "nomination", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"problem_log\").where(\"dt = current_date()\").writeTo(\"tv_shows_genre\").append()\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": [{"table": "tv_shows_genre", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dw.dw_orders_hourly (crossing, sex) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_orders_hourly", "columns": ["crossing", "sex"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"college\");\ndf.write().mode(\"overwrite\").saveAsTable(\"episodes\");\n", "labels": {"reads": [{"table": "college", "columns": null}], "writes": [{"table": "episodes", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model sustainability depends on winter_olympics\ndbt build --select sustainability --vars '{\"src\":\"winter_olympics\"}'\n", "labels": {"reads": [{"table": "winter_olympics", "columns": null}], "writes": [{"table": "sustainability", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO team SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart.campaigns_full --columns rental_date,dishid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart.campaigns_full", "columns": ["rental_date", "dishid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO disasters SELECT player_id, sustainability_score FROM dwd.coupon_use_full WHERE player_id > 41\")\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": ["player_id", "sustainability_score"]}], "writes": [{"table": "disasters", "columns": ["player_id", "sustainability_score"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO council_tax SELECT apid, daily_co2_emission FROM mart.mart_payments_df WHERE apid > 340\")\n", "labels": {"reads": [{"table": "mart.mart_payments_df", "columns": ["apid", "daily_co2_emission"]}], "writes": [{"table": "council_tax", "columns": ["apid", "daily_co2_emission"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dws.dws_users_hourly\").where(\"dt = current_date()\").writeTo(\"platform_production\").append()\n", "labels": {"reads": [{"table": "dws.dws_users_hourly", "columns": null}], "writes": [{"table": "platform_production", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"waste_generation_city_v2\").toPandas()\ndf[[\"eco_certified\", \"athlete_name\"]].to_sql(\"landfillcapacitybycountry\", engine, index=False)\n", "labels": {"reads": [{"table": "waste_generation_city_v2", "columns": null}], "writes": [{"table": "landfillcapacitybycountry", "columns": ["eco_certified", "athlete_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO artprograms SELECT is_organic, injury FROM ads.ads_products_full WHERE is_organic > 149\"\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": ["is_organic", "injury"]}], "writes": [{"table": "artprograms", "columns": ["is_organic", "injury"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"electricvehiclestats\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "electricvehiclestats", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT state_name, releaseyear FROM ods.ods_sessions_df\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"fault_log_parts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_sessions_df", "columns": ["state_name", "releaseyear"]}], "writes": [{"table": "fault_log_parts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"manager_award\").where(\"dt = current_date()\").writeTo(\"historicalcontexts\").append()\n", "labels": {"reads": [{"table": "manager_award", "columns": null}], "writes": [{"table": "historicalcontexts", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO total_capacity SELECT taxi_id, retailer, customer_address FROM factory_workers WHERE taxi_id > 205\"], check=True)\n", "labels": {"reads": [{"table": "factory_workers", "columns": ["taxi_id", "retailer", "customer_address"]}], "writes": [{"table": "total_capacity", "columns": ["taxi_id", "retailer", "customer_address"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO safety_incident SELECT * FROM legacy\ncur.execute(\"SELECT vendor_name, initiative_type FROM asia_events LIMIT 171\")\n", "labels": {"reads": [{"table": "asia_events", "columns": ["vendor_name", "initiative_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO show SELECT materialid, is_dessert, mining_operation FROM program_funding_2 WHERE materialid > 332\")\n", "labels": {"reads": [{"table": "program_funding_2", "columns": ["materialid", "is_dessert", "mining_operation"]}], "writes": [{"table": "show", "columns": ["materialid", "is_dessert", "mining_operation"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_chains\").toPandas()\ndf[[\"course_type\", \"zone_id\"]].to_sql(\"industrial_building_energy_efficiency\", engine, index=False)\n", "labels": {"reads": [{"table": "hotel_chains", "columns": null}], "writes": [{"table": "industrial_building_energy_efficiency", "columns": ["course_type", "zone_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO mart_exposure_hourly SELECT flight_number, last_used_bus, lot_id, dishid FROM ai_ethics WHERE flight_number > 192\");\n", "labels": {"reads": [{"table": "ai_ethics", "columns": ["flight_number", "last_used_bus", "lot_id", "dishid"]}], "writes": [{"table": "mart_exposure_hourly", "columns": ["flight_number", "last_used_bus", "lot_id", "dishid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT analysis_date, galleryid FROM stg.inventory_df LIMIT 310\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO clinics SELECT statement_details, category_id FROM marine_species_status WHERE statement_details > 230\")\n", "labels": {"reads": [{"table": "stg.inventory_df", "columns": ["analysis_date", "galleryid"]}, {"table": "marine_species_status", "columns": ["statement_details", "category_id"]}], "writes": [{"table": "clinics", "columns": ["statement_details", "category_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ods_member_point_full (depth, drug) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ods_member_point_full", "columns": ["depth", "drug"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"chemical_production_5\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nimport logging\nsql = \"INSERT INTO talent_acquisition SELECT a.well_type, b.author_id FROM episodes a JOIN ads.ads_exposure_di b ON a.sessiondate = b.sessiondate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "episodes", "columns": null}, {"table": "ads.ads_exposure_di", "columns": null}], "writes": [{"table": "talent_acquisition", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT num_beds, reported_date FROM displaced_people LIMIT 491\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "displaced_people", "columns": ["num_beds", "reported_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads.ads_cart_item_hourly (assessmentname, threats) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": ["assessmentname", "threats"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dwd_orders_di depends on sustainability\ndbt run -s dwd_orders_di --vars '{\"source_table\":\"sustainability\"}'\n", "labels": {"reads": [{"table": "sustainability", "columns": null}], "writes": [{"table": "dwd_orders_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.runtime > 387).all()\n# src table: busmaintenance\nengine.execute(\"INSERT INTO urban_farms SELECT * FROM busmaintenance\")\n", "labels": {"reads": [{"table": "busmaintenance", "columns": null}], "writes": [{"table": "urban_farms", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO cultural_competency SELECT characteristic_data_type, dose, treatment_type, session_language FROM france_culture WHERE characteristic_data_type > 214\");\n", "labels": {"reads": [{"table": "france_culture", "columns": ["characteristic_data_type", "dose", "treatment_type", "session_language"]}], "writes": [{"table": "cultural_competency", "columns": ["characteristic_data_type", "dose", "treatment_type", "session_language"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model paintings depends on animal_rehab\ndbt build -s paintings --vars 'source: animal_rehab'\n", "labels": {"reads": [{"table": "animal_rehab", "columns": null}], "writes": [{"table": "paintings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO club (electoral_register_id, country_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "club", "columns": ["electoral_register_id", "country_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO flight_emissions SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table diversity --columns investorgender,party_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "diversity", "columns": ["investorgender", "party_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM disaster_mitigation\"\n", "labels": {"reads": [{"table": "disaster_mitigation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ticketprice > 344).all()\n# src table: fossil_fuel_vehicles\nengine.execute(\"INSERT INTO workforcediversity SELECT * FROM fossil_fuel_vehicles\")\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": null}], "writes": [{"table": "workforcediversity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO creativeais SELECT * FROM legacy\ncur.execute(\"SELECT albumid, ticket_subject FROM document_types LIMIT 184\")\n", "labels": {"reads": [{"table": "document_types", "columns": ["albumid", "ticket_subject"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO player_attributes SELECT a.cargo_weight, b.area_size FROM donors_region a JOIN climateresearch b ON a.requestdate = b.requestdate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "donors_region", "columns": null}, {"table": "climateresearch", "columns": null}], "writes": [{"table": "player_attributes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO smartcitycosts SELECT race_ethnicity, assessment_score FROM socialimpactinvestments WHERE race_ethnicity > 10\");\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": ["race_ethnicity", "assessment_score"]}], "writes": [{"table": "smartcitycosts", "columns": ["race_ethnicity", "assessment_score"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_for_social_good\").toPandas()\ndf[[\"trip_duration\", \"service_type\"]].to_sql(\"volunteer_events\", engine, index=False)\n", "labels": {"reads": [{"table": "ai_for_social_good", "columns": null}], "writes": [{"table": "volunteer_events", "columns": ["trip_duration", "service_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO electronics_factories SELECT overall_rating, platformid, enr FROM sustainable_projects WHERE overall_rating > 99\"\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["overall_rating", "platformid", "enr"]}], "writes": [{"table": "electronics_factories", "columns": ["overall_rating", "platformid", "enr"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"artwork_styles\").toPandas()\ndf[[\"railway_id\", \"trainingdate\"]].to_sql(\"geologicalsurvey\", engine, index=False)\n", "labels": {"reads": [{"table": "artwork_styles", "columns": null}], "writes": [{"table": "geologicalsurvey", "columns": ["railway_id", "trainingdate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"perpetrator\");\ndf.write().mode(\"overwrite\").saveAsTable(\"electronics_factories\");\n", "labels": {"reads": [{"table": "perpetrator", "columns": null}], "writes": [{"table": "electronics_factories", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO founder SELECT a.plantlocation, b.transaction_type_description FROM ytterbiumproduction a JOIN crops_year b ON a.primaryaffiliation = b.primaryaffiliation\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ytterbiumproduction", "columns": null}, {"table": "crops_year", "columns": null}], "writes": [{"table": "founder", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sustainable_projects SELECT * FROM legacy\ncur.execute(\"SELECT fish_population, contributorname FROM stg.refunds_daily LIMIT 349\")\n", "labels": {"reads": [{"table": "stg.refunds_daily", "columns": ["fish_population", "contributorname"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM satellites\"\n", "labels": {"reads": [{"table": "satellites", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table ods.ods_sessions_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods.ods_sessions_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.mascot > 128).all()\n# src table: bi.bi_events_daily\nengine.execute(\"INSERT INTO multimodalhubs SELECT * FROM bi.bi_events_daily\")\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": null}], "writes": [{"table": "multimodalhubs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO yttrium_production (impact_score, entrydate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "yttrium_production", "columns": ["impact_score", "entrydate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO genetics.crispr SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO staff (diagnosis, trainingtitle) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "staff", "columns": ["diagnosis", "trainingtitle"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO organisations SELECT incident_region, event_date, post_category, violationid FROM ads_exposure_hourly WHERE incident_region > 109\"\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": ["incident_region", "event_date", "post_category", "violationid"]}], "writes": [{"table": "organisations", "columns": ["incident_region", "event_date", "post_category", "violationid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"exhibitiondetails\").where(\"dt = current_date()\").writeTo(\"languages\").append()\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": null}], "writes": [{"table": "languages", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT years_working, market_rate FROM vessel_positions LIMIT 403\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "vessel_positions", "columns": ["years_working", "market_rate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_events_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads_coupon_use_full SELECT a.hours_billed, b.therapist_id FROM solana_transactions a JOIN marine_species_indian b ON a.healthequitymetricscore = b.healthequitymetricscore\"\n", "labels": {"reads": [{"table": "solana_transactions", "columns": null}, {"table": "marine_species_indian", "columns": null}], "writes": [{"table": "ads_coupon_use_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 282;\nSQL\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": ["recipient_id", "therapy_type"]}, {"table": "forestry_practices", "columns": ["order_quantity", "support_rep_id", "lanes", "source_system_code"]}], "writes": [{"table": "show", "columns": ["order_quantity", "support_rep_id", "lanes", "source_system_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.start_time > 472).all()\n# src table: incarcerated\nengine.execute(\"INSERT INTO coffee_prices SELECT * FROM incarcerated\")\n", "labels": {"reads": [{"table": "incarcerated", "columns": null}], "writes": [{"table": "coffee_prices", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ingredients\", conn)\ndf.to_sql(\"machine_emissions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ingredients", "columns": null}], "writes": [{"table": "machine_emissions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"department_publications\").where(\"dt = current_date()\").writeTo(\"bi.bi_events_df\").append()\n", "labels": {"reads": [{"table": "department_publications", "columns": null}], "writes": [{"table": "bi.bi_events_df", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT serving_size, is_dessert FROM hires\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"useracct\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hires", "columns": ["serving_size", "is_dessert"]}], "writes": [{"table": "useracct", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bridgeconstruction\").where(\"dt = current_date()\").writeTo(\"rental\").append()\n", "labels": {"reads": [{"table": "bridgeconstruction", "columns": null}], "writes": [{"table": "rental", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT crop_name, hosts FROM accessible_tech_categories\", engine)\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"pharmasales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": ["crop_name", "hosts"]}], "writes": [{"table": "pharmasales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO red_line SELECT city_code, impressions FROM policyholders WHERE city_code > 486\"], check=True)\n", "labels": {"reads": [{"table": "policyholders", "columns": ["city_code", "impressions"]}], "writes": [{"table": "red_line", "columns": ["city_code", "impressions"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO volunteer_hours SELECT recruitername, garment_id, labor_practice FROM mart.mart_shipments_hourly WHERE recruitername > 294\"\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": ["recruitername", "garment_id", "labor_practice"]}], "writes": [{"table": "volunteer_hours", "columns": ["recruitername", "garment_id", "labor_practice"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO attack_outcomes (fairness_score, coownerid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "attack_outcomes", "columns": ["fairness_score", "coownerid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO agro_regions SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO nutrition_facts (membergender, biz_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "nutrition_facts", "columns": ["membergender", "biz_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO stg_users_daily SELECT name_full, stu_num, total_distance FROM co2price WHERE name_full > 182\"\n", "labels": {"reads": [{"table": "co2price", "columns": ["name_full", "stu_num", "total_distance"]}], "writes": [{"table": "stg_users_daily", "columns": ["name_full", "stu_num", "total_distance"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"races\").where(\"dt = current_date()\").writeTo(\"agriculturalinnovations\").append()\n", "labels": {"reads": [{"table": "races", "columns": null}], "writes": [{"table": "agriculturalinnovations", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM field_production\", conn)\ndf.to_sql(\"artworks\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "field_production", "columns": null}], "writes": [{"table": "artworks", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ref_shipping_agents\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"waste_generation_city_v2\")\n", "labels": {"reads": [{"table": "ref_shipping_agents", "columns": null}], "writes": [{"table": "waste_generation_city_v2", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"cinema\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"roles\")\n", "labels": {"reads": [{"table": "cinema", "columns": null}], "writes": [{"table": "roles", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO riskassessments (support_rep_id, taskdate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "riskassessments", "columns": ["support_rep_id", "taskdate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"e_scooter_trips\");\ndf.write().mode(\"overwrite\").saveAsTable(\"public_transportation_routes\");\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": null}], "writes": [{"table": "public_transportation_routes", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO auto_shows SELECT clicks, prepnurse, completion_year FROM sports_events WHERE clicks > 200\");\n", "labels": {"reads": [{"table": "sports_events", "columns": ["clicks", "prepnurse", "completion_year"]}], "writes": [{"table": "auto_shows", "columns": ["clicks", "prepnurse", "completion_year"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO teams_mascots (vol_id, crane_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "teams_mascots", "columns": ["vol_id", "crane_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table public_transportation_sydney --columns equipment,num_sessions --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "public_transportation_sydney", "columns": ["equipment", "num_sessions"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO facility SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"hotel_tech_adoptions\")\nsrc.write.insertInto(\"organic_cosmetics\", overwrite=True)\n", "labels": {"reads": [{"table": "hotel_tech_adoptions", "columns": null}], "writes": [{"table": "organic_cosmetics", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 320;\nEOF\n", "labels": {"reads": [{"table": "tourismproviders", "columns": ["max_salary", "completion_status", "peakhourid"]}], "writes": [{"table": "band", "columns": ["max_salary", "completion_status", "peakhourid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mining.company\").toPandas()\ndf[[\"change_date\", \"createdate\"]].to_sql(\"game_results\", engine, index=False)\n", "labels": {"reads": [{"table": "mining.company", "columns": null}], "writes": [{"table": "game_results", "columns": ["change_date", "createdate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 335;\nSQL\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": ["production_date", "calendar"]}, {"table": "reservations", "columns": ["num_shariah_compliant_investments", "quantity_sold", "main_services", "mission_id"]}], "writes": [{"table": "reporters", "columns": ["num_shariah_compliant_investments", "quantity_sold", "main_services", "mission_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO construction_labor_stats SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO social_good_projects SELECT a.lifespan, b.catalog_id FROM ref_shipping_agents a JOIN cosmetics.lipstick_spf_data b ON a.partner_id = b.partner_id\"\n", "labels": {"reads": [{"table": "ref_shipping_agents", "columns": null}, {"table": "cosmetics.lipstick_spf_data", "columns": null}], "writes": [{"table": "social_good_projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"therapy_sessions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"economic_diversification_argentina\")\n", "labels": {"reads": [{"table": "therapy_sessions", "columns": null}], "writes": [{"table": "economic_diversification_argentina", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO co2price SELECT annual_entry_exit, investor_id, working_year_starts, bank_name FROM satellitematerials WHERE annual_entry_exit > 8\"\n", "labels": {"reads": [{"table": "satellitematerials", "columns": ["annual_entry_exit", "investor_id", "working_year_starts", "bank_name"]}], "writes": [{"table": "co2price", "columns": ["annual_entry_exit", "investor_id", "working_year_starts", "bank_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table spacecraft_temperatures --columns prof_office,thefttype --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "spacecraft_temperatures", "columns": ["prof_office", "thefttype"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO eia_schedule SELECT programname, garment_name FROM disabilitysupportprograms WHERE programname > 107\"], check=True)\n", "labels": {"reads": [{"table": "disabilitysupportprograms", "columns": ["programname", "garment_name"]}], "writes": [{"table": "eia_schedule", "columns": ["programname", "garment_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO nba_games SELECT lesson_id, shop_name, classid FROM investment_rounds WHERE lesson_id > 482\");\n", "labels": {"reads": [{"table": "investment_rounds", "columns": ["lesson_id", "shop_name", "classid"]}], "writes": [{"table": "nba_games", "columns": ["lesson_id", "shop_name", "classid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO genre SELECT * FROM legacy\nspark.sql(\"INSERT INTO mineral_extraction_us SELECT visitor_id, date_left_staff, devices, tour_type FROM humanitarian_assistance WHERE visitor_id > 54\")\n", "labels": {"reads": [{"table": "humanitarian_assistance", "columns": ["visitor_id", "date_left_staff", "devices", "tour_type"]}], "writes": [{"table": "mineral_extraction_us", "columns": ["visitor_id", "date_left_staff", "devices", "tour_type"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mentalhealthparityviolations SELECT founder_group, document_code, ingredient FROM satelliteimagery WHERE founder_group > 196\"], check=True)\n", "labels": {"reads": [{"table": "satelliteimagery", "columns": ["founder_group", "document_code", "ingredient"]}], "writes": [{"table": "mentalhealthparityviolations", "columns": ["founder_group", "document_code", "ingredient"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO supply_chain SELECT complaint_id, host_city FROM dorm_amenity WHERE complaint_id > 4\"\n", "labels": {"reads": [{"table": "dorm_amenity", "columns": ["complaint_id", "host_city"]}], "writes": [{"table": "supply_chain", "columns": ["complaint_id", "host_city"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO shop SELECT * FROM legacy\ncur.execute(\"SELECT membergender, organization_id FROM status LIMIT 114\")\n", "labels": {"reads": [{"table": "status", "columns": ["membergender", "organization_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO startup_founders SELECT roomname, is_cruelty_free, fundingamount FROM criticalincidents WHERE roomname > 284\")\n", "labels": {"reads": [{"table": "criticalincidents", "columns": ["roomname", "is_cruelty_free", "fundingamount"]}], "writes": [{"table": "startup_founders", "columns": ["roomname", "is_cruelty_free", "fundingamount"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO assets_frameworks SELECT longitude, detention_type_code, other_characteristic_details, host_city_id FROM contract_timeline WHERE longitude > 243\"\n", "labels": {"reads": [{"table": "contract_timeline", "columns": ["longitude", "detention_type_code", "other_characteristic_details", "host_city_id"]}], "writes": [{"table": "assets_frameworks", "columns": ["longitude", "detention_type_code", "other_characteristic_details", "host_city_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"genetic.projects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "genetic.projects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"manufacturers\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "manufacturers", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO energy_efficiency_projects SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO rural_hospitals SELECT fund_id, propertyid FROM roller_coaster WHERE fund_id > 294\")\n", "labels": {"reads": [{"table": "roller_coaster", "columns": ["fund_id", "propertyid"]}], "writes": [{"table": "rural_hospitals", "columns": ["fund_id", "propertyid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO catalogs SELECT * FROM legacy\nspark.sql(\"INSERT INTO canals SELECT visitor_country, manufacturer_name FROM artistsales WHERE visitor_country > 1\")\n", "labels": {"reads": [{"table": "artistsales", "columns": ["visitor_country", "manufacturer_name"]}], "writes": [{"table": "canals", "columns": ["visitor_country", "manufacturer_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT round_amount, primary_conference FROM trains LIMIT 186\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "trains", "columns": ["round_amount", "primary_conference"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT contract_date, incident_type_code FROM invoice_lines\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"climatedata\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "invoice_lines", "columns": ["contract_date", "incident_type_code"]}], "writes": [{"table": "climatedata", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads_refunds_full\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"electoral_register\")\n", "labels": {"reads": [{"table": "ads_refunds_full", "columns": null}], "writes": [{"table": "electoral_register", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT neighborhood_id, ocean_name FROM bioprocesses\", engine)\nimport logging\ndf.to_sql(\"artists\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bioprocesses", "columns": ["neighborhood_id", "ocean_name"]}], "writes": [{"table": "artists", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table clinics_sa --columns billingcountry,budget_million --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "clinics_sa", "columns": ["billingcountry", "budget_million"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"menu_vendors\")\nsink_to_warehouse(df, \"stg.risk_score_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "menu_vendors", "columns": null}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO prescribes SELECT is_safe, support_rate, dockingid FROM food_safety_inspections WHERE is_safe > 88\");\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": ["is_safe", "support_rate", "dockingid"]}], "writes": [{"table": "prescribes", "columns": ["is_safe", "support_rate", "dockingid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"miningwaterusage\")\nexport_to_sink(df, \"courtcases\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "miningwaterusage", "columns": null}], "writes": [{"table": "courtcases", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO electricvehiclestats SELECT record_id, owner_id, experience_id FROM refugee_support WHERE record_id > 288\"\n", "labels": {"reads": [{"table": "refugee_support", "columns": ["record_id", "owner_id", "experience_id"]}], "writes": [{"table": "electricvehiclestats", "columns": ["record_id", "owner_id", "experience_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT billing, stu_hrs FROM community.donors\", engine)\nimport logging\ndf.to_sql(\"ods.ods_campaigns_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "community.donors", "columns": ["billing", "stu_hrs"]}], "writes": [{"table": "ods.ods_campaigns_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table emergencyservices --target-dir /tmp/land\n", "labels": {"reads": [{"table": "emergencyservices", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"train\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"research_vessels\")\n", "labels": {"reads": [{"table": "train", "columns": null}], "writes": [{"table": "research_vessels", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO dwd.coupon_use_daily SELECT contract_count, line_number FROM underwater_cables WHERE contract_count > 406\")\n", "labels": {"reads": [{"table": "underwater_cables", "columns": ["contract_count", "line_number"]}], "writes": [{"table": "dwd.coupon_use_daily", "columns": ["contract_count", "line_number"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"workers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ads.ads_vendors_hourly\")\n", "labels": {"reads": [{"table": "workers", "columns": null}], "writes": [{"table": "ads.ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM workout\"\n", "labels": {"reads": [{"table": "workout", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO volunteer_signups SELECT manufacturer, manager_id FROM shared_rides_tokyo WHERE manufacturer > 84\"], check=True)\n", "labels": {"reads": [{"table": "shared_rides_tokyo", "columns": ["manufacturer", "manager_id"]}], "writes": [{"table": "volunteer_signups", "columns": ["manufacturer", "manager_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO product_info (roomid, stayid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "product_info", "columns": ["roomid", "stayid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT rate, charging_level FROM seamounts LIMIT 373\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO conditions SELECT stu_num, animal_species FROM city_department WHERE stu_num > 207\")\n", "labels": {"reads": [{"table": "seamounts", "columns": ["rate", "charging_level"]}, {"table": "city_department", "columns": ["stu_num", "animal_species"]}], "writes": [{"table": "conditions", "columns": ["stu_num", "animal_species"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"features\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"org_comms\")\n", "labels": {"reads": [{"table": "features", "columns": null}], "writes": [{"table": "org_comms", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"therapy_sessions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"traditionalarts\")\n", "labels": {"reads": [{"table": "therapy_sessions", "columns": null}], "writes": [{"table": "traditionalarts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"safetyorgs\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"habitats\")\n", "labels": {"reads": [{"table": "safetyorgs", "columns": null}], "writes": [{"table": "habitats", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO dws.dws_coupon_use_di SELECT card_id, purchase_date, contributorname, avg_usage FROM water_sources WHERE card_id > 126\")\n", "labels": {"reads": [{"table": "water_sources", "columns": ["card_id", "purchase_date", "contributorname", "avg_usage"]}], "writes": [{"table": "dws.dws_coupon_use_di", "columns": ["card_id", "purchase_date", "contributorname", "avg_usage"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO diversity SELECT address, attorney_id FROM asia_events WHERE address > 480\")\n", "labels": {"reads": [{"table": "asia_events", "columns": ["address", "attorney_id"]}], "writes": [{"table": "diversity", "columns": ["address", "attorney_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mineral_extraction_us\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "mineral_extraction_us", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO race_ethnicity (virtual_tour_sessions, profits_in_billion) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "race_ethnicity", "columns": ["virtual_tour_sessions", "profits_in_billion"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sustainable_projects SELECT * FROM legacy\ncur.execute(\"SELECT contractor_id, billing_city FROM hydro_plants LIMIT 274\")\n", "labels": {"reads": [{"table": "hydro_plants", "columns": ["contractor_id", "billing_city"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO researchprojects SELECT materialname, shippeddate FROM transportation_union WHERE materialname > 316\"\n", "labels": {"reads": [{"table": "transportation_union", "columns": ["materialname", "shippeddate"]}], "writes": [{"table": "researchprojects", "columns": ["materialname", "shippeddate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"energy_storage\").toPandas()\ndf[[\"class\", \"fish_count\"]].to_sql(\"climate_finance_asia\", engine, index=False)\n", "labels": {"reads": [{"table": "energy_storage", "columns": null}], "writes": [{"table": "climate_finance_asia", "columns": ["class", "fish_count"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM cybersecurity.strategies\", conn)\ndf.to_sql(\"farmers_india\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "cybersecurity.strategies", "columns": null}], "writes": [{"table": "farmers_india", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"job_change\").where(\"dt = current_date()\").writeTo(\"races\").append()\n", "labels": {"reads": [{"table": "job_change", "columns": null}], "writes": [{"table": "races", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO defense_diplomacy SELECT units_sold, partitionid, invoice_date FROM biomes WHERE units_sold > 235\")\n", "labels": {"reads": [{"table": "biomes", "columns": ["units_sold", "partitionid", "invoice_date"]}], "writes": [{"table": "defense_diplomacy", "columns": ["units_sold", "partitionid", "invoice_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"city_properties\").where(\"dt = current_date()\").writeTo(\"satellitematerials\").append()\n", "labels": {"reads": [{"table": "city_properties", "columns": null}], "writes": [{"table": "satellitematerials", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marketing_budgets\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"creativeais\")\n", "labels": {"reads": [{"table": "marketing_budgets", "columns": null}], "writes": [{"table": "creativeais", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT physical, character FROM mart_exposure_di LIMIT 168\")\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO cargos SELECT rooms, archaeologist_id FROM fishcaught WHERE rooms > 430\")\n", "labels": {"reads": [{"table": "mart_exposure_di", "columns": ["physical", "character"]}, {"table": "fishcaught", "columns": ["rooms", "archaeologist_id"]}], "writes": [{"table": "cargos", "columns": ["rooms", "archaeologist_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT traveler_id, home_team_score FROM construction_union LIMIT 338\")\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO marketingbudget SELECT total, tourists, payment_date, investment FROM refugee_support WHERE total > 181\")\n", "labels": {"reads": [{"table": "construction_union", "columns": ["traveler_id", "home_team_score"]}, {"table": "refugee_support", "columns": ["total", "tourists", "payment_date", "investment"]}], "writes": [{"table": "marketingbudget", "columns": ["total", "tourists", "payment_date", "investment"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"roads\")\ndump_to_sink(df, \"rural.bus_trips\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "roads", "columns": null}], "writes": [{"table": "rural.bus_trips", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ref_colors\");\ndf.write().mode(\"overwrite\").saveAsTable(\"florida_conservation_initiatives\");\n", "labels": {"reads": [{"table": "ref_colors", "columns": null}], "writes": [{"table": "florida_conservation_initiatives", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT astronaut_name, competition_type FROM concert_events LIMIT 442\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "concert_events", "columns": ["astronaut_name", "competition_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO ads_coupon_use_full SELECT pet_age, drug_id, access_count FROM maintenance WHERE pet_age > 387\");\n", "labels": {"reads": [{"table": "maintenance", "columns": ["pet_age", "drug_id", "access_count"]}], "writes": [{"table": "ads_coupon_use_full", "columns": ["pet_age", "drug_id", "access_count"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM postseason\"\n", "labels": {"reads": [{"table": "postseason", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_vendors_daily\").toPandas()\ndf[[\"date_valid_to\", \"underrepresented_community\"]].to_sql(\"bi.bi_risk_score_full\", engine, index=False)\n", "labels": {"reads": [{"table": "ods_vendors_daily", "columns": null}], "writes": [{"table": "bi.bi_risk_score_full", "columns": ["date_valid_to", "underrepresented_community"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_leaders\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "community_leaders", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO art_pieces SELECT wellid, workshop_name FROM union_membership WHERE wellid > 247\"\n", "labels": {"reads": [{"table": "union_membership", "columns": ["wellid", "workshop_name"]}], "writes": [{"table": "art_pieces", "columns": ["wellid", "workshop_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ads.ads_cart_item_hourly\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.dwd_device_log_delta\", conn)\ndf.to_sql(\"cyber_incidents\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": null}], "writes": [{"table": "cyber_incidents", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.vessel > 245).all()\n# src table: mappinglengths\nengine.execute(\"INSERT INTO property_community SELECT * FROM mappinglengths\")\n", "labels": {"reads": [{"table": "mappinglengths", "columns": null}], "writes": [{"table": "property_community", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fault_log\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"articles\")\n", "labels": {"reads": [{"table": "fault_log", "columns": null}], "writes": [{"table": "articles", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 338;\nSQL\n", "labels": {"reads": [{"table": "therapy_attendance", "columns": ["stop", "fan_age"]}, {"table": "ads_sessions_di", "columns": ["race_ethnicity", "contractorid"]}], "writes": [{"table": "africa_schema.african_mines", "columns": ["race_ethnicity", "contractorid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO dw.dw_coupon_use_daily SELECT famous_title, lname, technology, materialname FROM therapy_attendance WHERE famous_title > 285\")\n", "labels": {"reads": [{"table": "therapy_attendance", "columns": ["famous_title", "lname", "technology", "materialname"]}], "writes": [{"table": "dw.dw_coupon_use_daily", "columns": ["famous_title", "lname", "technology", "materialname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 23;\nEOF\n", "labels": {"reads": [{"table": "contract_transactions", "columns": ["order_status_code", "temporary_acting"]}], "writes": [{"table": "animal_budget", "columns": ["order_status_code", "temporary_acting"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table equipment_sales --target-dir /tmp/land\n", "labels": {"reads": [{"table": "equipment_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"train_station\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "train_station", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"customer_policies\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"fair_trade_brands\")\n", "labels": {"reads": [{"table": "customer_policies", "columns": null}], "writes": [{"table": "fair_trade_brands", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ucl_top10\").toPandas()\ndf[[\"marketing_region_name\", \"mh_id\"]].to_sql(\"defense_contracts_v2\", engine, index=False)\n", "labels": {"reads": [{"table": "ucl_top10", "columns": null}], "writes": [{"table": "defense_contracts_v2", "columns": ["marketing_region_name", "mh_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO wine SELECT investor_name, founder_gender, impact_id, regional_population FROM airlines WHERE investor_name > 202\");\n", "labels": {"reads": [{"table": "airlines", "columns": ["investor_name", "founder_gender", "impact_id", "regional_population"]}], "writes": [{"table": "wine", "columns": ["investor_name", "founder_gender", "impact_id", "regional_population"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"freshwater_fish_farms\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stateinfrastructure\")\n", "labels": {"reads": [{"table": "freshwater_fish_farms", "columns": null}], "writes": [{"table": "stateinfrastructure", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi.device_log SELECT * FROM legacy\ncur.execute(\"SELECT drugname, practiceid FROM ap_budget LIMIT 269\")\n", "labels": {"reads": [{"table": "ap_budget", "columns": ["drugname", "practiceid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT access_date, count_id FROM eu_humanitarian_assistance LIMIT 110\")\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ads_payments_hourly SELECT custid, donorage, rainfall FROM chemical_composition WHERE custid > 400\")\n", "labels": {"reads": [{"table": "eu_humanitarian_assistance", "columns": ["access_date", "count_id"]}, {"table": "chemical_composition", "columns": ["custid", "donorage", "rainfall"]}], "writes": [{"table": "ads_payments_hourly", "columns": ["custid", "donorage", "rainfall"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO biotech_startups SELECT dockingid, supplierid, annual_entry_exit, excavation_site FROM manufacturingplants WHERE dockingid > 492\");\n", "labels": {"reads": [{"table": "manufacturingplants", "columns": ["dockingid", "supplierid", "annual_entry_exit", "excavation_site"]}], "writes": [{"table": "biotech_startups", "columns": ["dockingid", "supplierid", "annual_entry_exit", "excavation_site"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_shipments_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"wastewater_facilities\")\n", "labels": {"reads": [{"table": "bi_shipments_daily", "columns": null}], "writes": [{"table": "wastewater_facilities", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"host\")\nsrc.write.insertInto(\"policyholders\", overwrite=True)\n", "labels": {"reads": [{"table": "host", "columns": null}], "writes": [{"table": "policyholders", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM program_history\", conn)\ndf.to_sql(\"intelligence_personnel\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "program_history", "columns": null}], "writes": [{"table": "intelligence_personnel", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"haircare_sales\").where(\"dt = current_date()\").writeTo(\"autoshow\").append()\n", "labels": {"reads": [{"table": "haircare_sales", "columns": null}], "writes": [{"table": "autoshow", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"astronaut_missions\")\nsrc.write.insertInto(\"carbon_prices_3\", overwrite=True)\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": null}], "writes": [{"table": "carbon_prices_3", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO playergamedata (faculty, founder_gender) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "playergamedata", "columns": ["faculty", "founder_gender"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO cyber_incidents SELECT * FROM legacy\nspark.sql(\"INSERT INTO mentalhealthscores SELECT co2_emission, extraction_state FROM dwd.dwd_member_point_di WHERE co2_emission > 488\")\n", "labels": {"reads": [{"table": "dwd.dwd_member_point_di", "columns": ["co2_emission", "extraction_state"]}], "writes": [{"table": "mentalhealthscores", "columns": ["co2_emission", "extraction_state"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hair_care_sales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ocean_pollution\")\n", "labels": {"reads": [{"table": "hair_care_sales", "columns": null}], "writes": [{"table": "ocean_pollution", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"brand_info\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "brand_info", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO haircaresales (forename, ingredient_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "haircaresales", "columns": ["forename", "ingredient_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dws_events_di depends on show\ndbt run --models dws_events_di --vars '{\"src\":\"show\"}'\n", "labels": {"reads": [{"table": "show", "columns": null}], "writes": [{"table": "dws_events_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ref_attraction_types\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "ref_attraction_types", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO student_tests_taken SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"training_programs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "training_programs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO laborstatistics (material_name, resource_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "laborstatistics", "columns": ["material_name", "resource_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO salary SELECT sales_billion, date_from, quality FROM artists WHERE sales_billion > 285\"], check=True)\n", "labels": {"reads": [{"table": "artists", "columns": ["sales_billion", "date_from", "quality"]}], "writes": [{"table": "salary", "columns": ["sales_billion", "date_from", "quality"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stg.sessions_full\");\ndf.write().mode(\"overwrite\").saveAsTable(\"has_amenity\");\n", "labels": {"reads": [{"table": "stg.sessions_full", "columns": null}], "writes": [{"table": "has_amenity", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO community_policing SELECT * FROM legacy\nspark.sql(\"INSERT INTO nursing_homes SELECT productlaunchdate, strategy, country_name, build_year FROM manager WHERE productlaunchdate > 164\")\n", "labels": {"reads": [{"table": "manager", "columns": ["productlaunchdate", "strategy", "country_name", "build_year"]}], "writes": [{"table": "nursing_homes", "columns": ["productlaunchdate", "strategy", "country_name", "build_year"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd_payments_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bi.refunds_daily\")\n", "labels": {"reads": [{"table": "dwd_payments_delta", "columns": null}], "writes": [{"table": "bi.refunds_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"enrollments\");\ndf.write().mode(\"overwrite\").saveAsTable(\"stg.stg_events_hourly\");\n", "labels": {"reads": [{"table": "enrollments", "columns": null}], "writes": [{"table": "stg.stg_events_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO strains SELECT * FROM legacy\nspark.sql(\"INSERT INTO youth_fan_participation SELECT grant_date, source_system_code FROM workforce_development_programs WHERE grant_date > 463\")\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": ["grant_date", "source_system_code"]}], "writes": [{"table": "youth_fan_participation", "columns": ["grant_date", "source_system_code"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO mart.mart_products_hourly SELECT cuisine, partnership_id, artworkname, height FROM ancient_artifacts WHERE cuisine > 219\")\n", "labels": {"reads": [{"table": "ancient_artifacts", "columns": ["cuisine", "partnership_id", "artworkname", "height"]}], "writes": [{"table": "mart.mart_products_hourly", "columns": ["cuisine", "partnership_id", "artworkname", "height"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.mart_campaigns_daily SELECT * FROM legacy\ncur.execute(\"SELECT sportname, education_id FROM tennis_players LIMIT 411\")\n", "labels": {"reads": [{"table": "tennis_players", "columns": ["sportname", "education_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO sports_events SELECT calendar_date, followers, donor_name FROM mart_payments_df WHERE calendar_date > 437\");\n", "labels": {"reads": [{"table": "mart_payments_df", "columns": ["calendar_date", "followers", "donor_name"]}], "writes": [{"table": "sports_events", "columns": ["calendar_date", "followers", "donor_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"music_events\").where(\"dt = current_date()\").writeTo(\"virtual_visitors\").append()\n", "labels": {"reads": [{"table": "music_events", "columns": null}], "writes": [{"table": "virtual_visitors", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 289;\nEOF\n", "labels": {"reads": [{"table": "match", "columns": ["total_cost", "booking_date", "individual_first_name", "vaccine_type"]}], "writes": [{"table": "video_content", "columns": ["total_cost", "booking_date", "individual_first_name", "vaccine_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM workersalaries\"\n", "labels": {"reads": [{"table": "workersalaries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dws.inventory_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dws.inventory_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO diversion_programs SELECT order_shipping_charges, bikes_available, student_details, registration_date FROM concert_revenue WHERE order_shipping_charges > 239\"\n", "labels": {"reads": [{"table": "concert_revenue", "columns": ["order_shipping_charges", "bikes_available", "student_details", "registration_date"]}], "writes": [{"table": "diversion_programs", "columns": ["order_shipping_charges", "bikes_available", "student_details", "registration_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"geological_survey\");\ndf.write().mode(\"overwrite\").saveAsTable(\"regional_archaeologists\");\n", "labels": {"reads": [{"table": "geological_survey", "columns": null}], "writes": [{"table": "regional_archaeologists", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model fairtradecertification depends on rd_expenditure\ndbt build --select fairtradecertification --vars '{\"src\":\"rd_expenditure\"}'\n", "labels": {"reads": [{"table": "rd_expenditure", "columns": null}], "writes": [{"table": "fairtradecertification", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_acidification\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"economic_diversification\")\n", "labels": {"reads": [{"table": "ocean_acidification", "columns": null}], "writes": [{"table": "economic_diversification", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO event SELECT uses_vr, bdate, request, inclusive FROM dwd.dwd_products WHERE uses_vr > 135\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": ["uses_vr", "bdate", "request", "inclusive"]}], "writes": [{"table": "event", "columns": ["uses_vr", "bdate", "request", "inclusive"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO test_drives SELECT mode, dept_store_chain_id FROM recyclingratessouthamerica WHERE mode > 1\")\n", "labels": {"reads": [{"table": "recyclingratessouthamerica", "columns": ["mode", "dept_store_chain_id"]}], "writes": [{"table": "test_drives", "columns": ["mode", "dept_store_chain_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"astronauts\").where(\"dt = current_date()\").writeTo(\"appliances\").append()\n", "labels": {"reads": [{"table": "astronauts", "columns": null}], "writes": [{"table": "appliances", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT bedtype, user_category FROM tourism_activities LIMIT 496\")\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO dw_member_point_full SELECT venue, attorney_last_name, functional_area_description, sentence_length FROM water_sources WHERE venue > 489\")\n", "labels": {"reads": [{"table": "tourism_activities", "columns": ["bedtype", "user_category"]}, {"table": "water_sources", "columns": ["venue", "attorney_last_name", "functional_area_description", "sentence_length"]}], "writes": [{"table": "dw_member_point_full", "columns": ["venue", "attorney_last_name", "functional_area_description", "sentence_length"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fairness_scores SELECT a.firstname, b.protected FROM stg.stg_users_di a JOIN leo_missions b ON a.hiredate = b.hiredate\"\n", "labels": {"reads": [{"table": "stg.stg_users_di", "columns": null}, {"table": "leo_missions", "columns": null}], "writes": [{"table": "fairness_scores", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO facility SELECT skill_description, participant_type_code, sport_id, temperature FROM program_funding_2 WHERE skill_description > 261\")\n", "labels": {"reads": [{"table": "program_funding_2", "columns": ["skill_description", "participant_type_code", "sport_id", "temperature"]}], "writes": [{"table": "facility", "columns": ["skill_description", "participant_type_code", "sport_id", "temperature"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO iot_sensors SELECT num_cases, assigned_to_staff_id, calendar_date, census_ranking FROM sustainable_menu_items WHERE num_cases > 422\"\n", "labels": {"reads": [{"table": "sustainable_menu_items", "columns": ["num_cases", "assigned_to_staff_id", "calendar_date", "census_ranking"]}], "writes": [{"table": "iot_sensors", "columns": ["num_cases", "assigned_to_staff_id", "calendar_date", "census_ranking"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO publications SELECT gross_worldwide, hours FROM team WHERE gross_worldwide > 400\"\n", "labels": {"reads": [{"table": "team", "columns": ["gross_worldwide", "hours"]}], "writes": [{"table": "publications", "columns": ["gross_worldwide", "hours"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"draft_copies\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"smart_contracts_transactions\")\n", "labels": {"reads": [{"table": "draft_copies", "columns": null}], "writes": [{"table": "smart_contracts_transactions", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"erc20_transactions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"grants\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "grants", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"excavations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"shipments\")\n", "labels": {"reads": [{"table": "excavations", "columns": null}], "writes": [{"table": "shipments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table checking --target-dir /tmp/land\n", "labels": {"reads": [{"table": "checking", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO vessel_positions SELECT a.itemid, b.is_organic FROM intelligencesatellites a JOIN maintenancerequests b ON a.activity_type = b.activity_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "intelligencesatellites", "columns": null}, {"table": "maintenancerequests", "columns": null}], "writes": [{"table": "vessel_positions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO housingaffordability SELECT a.component_name, b.type_of_thing_code FROM pacific_ocean a JOIN useracct b ON a.wastetype = b.wastetype\"\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": null}, {"table": "useracct", "columns": null}], "writes": [{"table": "housingaffordability", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM user_profiles\"\n", "labels": {"reads": [{"table": "user_profiles", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 277;\nSQL\n", "labels": {"reads": [{"table": "leo_missions", "columns": ["founders", "state_id"]}, {"table": "people", "columns": ["dept_address", "nutrient_level", "training_name", "investment"]}], "writes": [{"table": "policyanalysis", "columns": ["dept_address", "nutrient_level", "training_name", "investment"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table pacific_ocean --target-dir /tmp/land\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fleets\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"reservations\")\n", "labels": {"reads": [{"table": "fleets", "columns": null}], "writes": [{"table": "reservations", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"platform\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "platform", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO nutrition_facts SELECT ngo_name, financially_capable, membership_card, claim_amount FROM airportdata WHERE ngo_name > 393\"\n", "labels": {"reads": [{"table": "airportdata", "columns": ["ngo_name", "financially_capable", "membership_card", "claim_amount"]}], "writes": [{"table": "nutrition_facts", "columns": ["ngo_name", "financially_capable", "membership_card", "claim_amount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"professor\")\nexport_to_output(df, \"circular_economy_companies\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "professor", "columns": null}], "writes": [{"table": "circular_economy_companies", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO stg.sessions_full SELECT vehicle_id, issue_date, sustainable FROM criminal_justice_reform_initiatives WHERE vehicle_id > 311\")\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": ["vehicle_id", "issue_date", "sustainable"]}], "writes": [{"table": "stg.sessions_full", "columns": ["vehicle_id", "issue_date", "sustainable"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"production_data\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"product_characteristics\")\n", "labels": {"reads": [{"table": "production_data", "columns": null}], "writes": [{"table": "product_characteristics", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"accelerator_compatible_browser\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.member_point_df\")\n", "labels": {"reads": [{"table": "accelerator_compatible_browser", "columns": null}], "writes": [{"table": "mart.member_point_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO representative SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO virtual_tours SELECT enable_location_tracking, season_number, num_hotels FROM furniture WHERE enable_location_tracking > 353\"\n", "labels": {"reads": [{"table": "furniture", "columns": ["enable_location_tracking", "season_number", "num_hotels"]}], "writes": [{"table": "virtual_tours", "columns": ["enable_location_tracking", "season_number", "num_hotels"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mart.mart_shipments_hourly\").where(\"dt = current_date()\").writeTo(\"community_policing\").append()\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": null}], "writes": [{"table": "community_policing", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 199;\nSQL\n", "labels": {"reads": [{"table": "smart_grids", "columns": ["individual_last_name", "views"]}, {"table": "states", "columns": ["number_of_hosts", "communityid", "userid"]}], "writes": [{"table": "regional_railways", "columns": ["number_of_hosts", "communityid", "userid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"timed_status_of_things\");\ndf.write().mode(\"overwrite\").saveAsTable(\"accessibility_audits\");\n", "labels": {"reads": [{"table": "timed_status_of_things", "columns": null}], "writes": [{"table": "accessibility_audits", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT rig_id, sale_quantity FROM viewership LIMIT 189\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "viewership", "columns": ["rig_id", "sale_quantity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"space_telescopes\");\ndf.write().mode(\"overwrite\").saveAsTable(\"phishing_attempts\");\n", "labels": {"reads": [{"table": "space_telescopes", "columns": null}], "writes": [{"table": "phishing_attempts", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO fireincidents (potency, facid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "fireincidents", "columns": ["potency", "facid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_contracts_v2\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "defense_contracts_v2", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"community_education_programs\")\nexport_to_output(df, \"social_good_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "community_education_programs", "columns": null}], "writes": [{"table": "social_good_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"unions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"person\")\n", "labels": {"reads": [{"table": "unions", "columns": null}], "writes": [{"table": "person", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO levees SELECT performance_id, tournament_name, complete_date FROM storage WHERE performance_id > 422\");\n", "labels": {"reads": [{"table": "storage", "columns": ["performance_id", "tournament_name", "complete_date"]}], "writes": [{"table": "levees", "columns": ["performance_id", "tournament_name", "complete_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_cart_item_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws.dws_cart_item_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO ads.ads_member_point_daily SELECT amount_waste, research_id, trips_on_day, rating_in_percent FROM birds WHERE amount_waste > 196\");\n", "labels": {"reads": [{"table": "birds", "columns": ["amount_waste", "research_id", "trips_on_day", "rating_in_percent"]}], "writes": [{"table": "ads.ads_member_point_daily", "columns": ["amount_waste", "research_id", "trips_on_day", "rating_in_percent"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO survey_data SELECT num_of_component, credit_score, fastestlapspeed, socially_responsible FROM landfillcapacitybycountry WHERE num_of_component > 185\");\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": ["num_of_component", "credit_score", "fastestlapspeed", "socially_responsible"]}], "writes": [{"table": "survey_data", "columns": ["num_of_component", "credit_score", "fastestlapspeed", "socially_responsible"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM course\"\n", "labels": {"reads": [{"table": "course", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT matchid, employment_id FROM premises LIMIT 112\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "premises", "columns": ["matchid", "employment_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO government_funding SELECT a.writer, b.yield_id FROM defense_contracts_v2 a JOIN incarcerated b ON a.status = b.status\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "defense_contracts_v2", "columns": null}, {"table": "incarcerated", "columns": null}], "writes": [{"table": "government_funding", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table salinity_readings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "salinity_readings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"defense_spending\")\nwrite_to_sink(df, \"material\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "defense_spending", "columns": null}], "writes": [{"table": "material", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 244;\nEOF\n", "labels": {"reads": [{"table": "cycling", "columns": ["attack_country", "artworkname", "event_date"]}], "writes": [{"table": "artprograms", "columns": ["attack_country", "artworkname", "event_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"research.species\")\nsrc.write.insertInto(\"singer_in_concert\", overwrite=True)\n", "labels": {"reads": [{"table": "research.species", "columns": null}], "writes": [{"table": "singer_in_concert", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"public_participation\")\npersist_to_store(df, \"hires\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "public_participation", "columns": null}], "writes": [{"table": "hires", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO cargo_equipment SELECT crime_date, garment_material FROM enzyme WHERE crime_date > 459\"\n", "labels": {"reads": [{"table": "enzyme", "columns": ["crime_date", "garment_material"]}], "writes": [{"table": "cargo_equipment", "columns": ["crime_date", "garment_material"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"gamedesign\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "gamedesign", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model healthydelights depends on ods_vendors_daily\ndbt run --select healthydelights --vars '{\"source_table\":\"ods_vendors_daily\"}'\n", "labels": {"reads": [{"table": "ods_vendors_daily", "columns": null}], "writes": [{"table": "healthydelights", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"customer_contact_channels\").where(\"dt = current_date()\").writeTo(\"catalog_contents\").append()\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": null}], "writes": [{"table": "catalog_contents", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT catalog_entry_id, projectid FROM ocean_acidity LIMIT 30\")\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO round SELECT water_temp, donation_date, funding_round_id, launched_year FROM opioid_overdoses WHERE water_temp > 335\")\n", "labels": {"reads": [{"table": "ocean_acidity", "columns": ["catalog_entry_id", "projectid"]}, {"table": "opioid_overdoses", "columns": ["water_temp", "donation_date", "funding_round_id", "launched_year"]}], "writes": [{"table": "round", "columns": ["water_temp", "donation_date", "funding_round_id", "launched_year"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT grant_start_date, farmer_name FROM dwd.dwd_member_point_di\", engine)\nimport logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"waterconservationbudget\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_member_point_di", "columns": ["grant_start_date", "farmer_name"]}], "writes": [{"table": "waterconservationbudget", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO basketball_teams SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"classroom\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "classroom", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO geological_survey SELECT wellid, contract_type FROM development_hours WHERE wellid > 422\"\n", "labels": {"reads": [{"table": "development_hours", "columns": ["wellid", "contract_type"]}], "writes": [{"table": "geological_survey", "columns": ["wellid", "contract_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods.ods_events_daily SELECT * FROM legacy\ncur.execute(\"SELECT do_value, area_sqkm FROM shark_biomass LIMIT 463\")\n", "labels": {"reads": [{"table": "shark_biomass", "columns": ["do_value", "area_sqkm"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO matches SELECT review_text, regionname, savingsid, book_club_id FROM haircaresales WHERE review_text > 252\"\n", "labels": {"reads": [{"table": "haircaresales", "columns": ["review_text", "regionname", "savingsid", "book_club_id"]}], "writes": [{"table": "matches", "columns": ["review_text", "regionname", "savingsid", "book_club_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ocean_floor_mapping\");\ndf.write().mode(\"overwrite\").saveAsTable(\"market\");\n", "labels": {"reads": [{"table": "ocean_floor_mapping", "columns": null}], "writes": [{"table": "market", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO social_good_education (supplier_name, issue_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "social_good_education", "columns": ["supplier_name", "issue_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO orgdonations SELECT animal_type, credits, task, org_id FROM infection_rates WHERE animal_type > 95\");\n", "labels": {"reads": [{"table": "infection_rates", "columns": ["animal_type", "credits", "task", "org_id"]}], "writes": [{"table": "orgdonations", "columns": ["animal_type", "credits", "task", "org_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mars_rovers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"climatedata\")\n", "labels": {"reads": [{"table": "mars_rovers", "columns": null}], "writes": [{"table": "climatedata", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO police_officers_tx SELECT mission_date, nutrient_level FROM dw.dw_products_delta WHERE mission_date > 426\"\n", "labels": {"reads": [{"table": "dw.dw_products_delta", "columns": ["mission_date", "nutrient_level"]}], "writes": [{"table": "police_officers_tx", "columns": ["mission_date", "nutrient_level"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO dorm_amenity SELECT devices, date_valid_to, height_feet FROM support_groups WHERE devices > 159\"\n", "labels": {"reads": [{"table": "support_groups", "columns": ["devices", "date_valid_to", "height_feet"]}], "writes": [{"table": "dorm_amenity", "columns": ["devices", "date_valid_to", "height_feet"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO hotel_revenue SELECT a.union_members, b.customer_name FROM ods.sessions_daily a JOIN biosensors.projects b ON a.purchase_transaction_id = b.purchase_transaction_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": null}, {"table": "biosensors.projects", "columns": null}], "writes": [{"table": "hotel_revenue", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table vehicle_counts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "vehicle_counts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO creative_ai_applications SELECT art, start_time, attendeename, organizationname FROM bustrips WHERE art > 98\"\n", "labels": {"reads": [{"table": "bustrips", "columns": ["art", "start_time", "attendeename", "organizationname"]}], "writes": [{"table": "creative_ai_applications", "columns": ["art", "start_time", "attendeename", "organizationname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO astronauts SELECT productionid, pettype, worker_name, active_to_date FROM founder WHERE productionid > 352\")\n", "labels": {"reads": [{"table": "founder", "columns": ["productionid", "pettype", "worker_name", "active_to_date"]}], "writes": [{"table": "astronauts", "columns": ["productionid", "pettype", "worker_name", "active_to_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"aus_wellbeing\").where(\"dt = current_date()\").writeTo(\"dwd.dwd_events_delta\").append()\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": null}], "writes": [{"table": "dwd.dwd_events_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"traffic\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "traffic", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"veteran_occupations\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "veteran_occupations", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO medical_facilities_nyc SELECT sensor_id, vehicle_type FROM disinformation_detection WHERE sensor_id > 155\"], check=True)\n", "labels": {"reads": [{"table": "disinformation_detection", "columns": ["sensor_id", "vehicle_type"]}], "writes": [{"table": "medical_facilities_nyc", "columns": ["sensor_id", "vehicle_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table provider_training --target-dir /tmp/land\n", "labels": {"reads": [{"table": "provider_training", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mental_health_clinics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"playergamehistory\")\n", "labels": {"reads": [{"table": "mental_health_clinics", "columns": null}], "writes": [{"table": "playergamehistory", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"bridge\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"shariah_compliant_loans\")\n", "labels": {"reads": [{"table": "bridge", "columns": null}], "writes": [{"table": "shariah_compliant_loans", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO plots SELECT aid, cmi_details FROM mental_health_clinics WHERE aid > 442\")\n", "labels": {"reads": [{"table": "mental_health_clinics", "columns": ["aid", "cmi_details"]}], "writes": [{"table": "plots", "columns": ["aid", "cmi_details"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO higher_ed.students SELECT vulnerability, bridgetype, grant_id FROM body_builder WHERE vulnerability > 116\")\n", "labels": {"reads": [{"table": "body_builder", "columns": ["vulnerability", "bridgetype", "grant_id"]}], "writes": [{"table": "higher_ed.students", "columns": ["vulnerability", "bridgetype", "grant_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.shipments_df\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"timber_production\")\n", "labels": {"reads": [{"table": "mart.shipments_df", "columns": null}], "writes": [{"table": "timber_production", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT crop_name, dept_code FROM ads.ads_vendors_hourly LIMIT 261\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [{"table": "ads.ads_vendors_hourly", "columns": ["crop_name", "dept_code"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT compatible_since_year, acidification_level FROM ref_locations LIMIT 226\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "ref_locations", "columns": ["compatible_since_year", "acidification_level"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT cmi_cross_ref_id, headquarters FROM satellitedata\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"music\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "satellitedata", "columns": ["cmi_cross_ref_id", "headquarters"]}], "writes": [{"table": "music", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO freshwater_fish_farms SELECT district_id, energy_efficiency_rating, athlete_id FROM editor WHERE district_id > 473\"\n", "labels": {"reads": [{"table": "editor", "columns": ["district_id", "energy_efficiency_rating", "athlete_id"]}], "writes": [{"table": "freshwater_fish_farms", "columns": ["district_id", "energy_efficiency_rating", "athlete_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model basketball_teams depends on bi.device_log_hourly\ndbt run --select basketball_teams --vars '{\"src\":\"bi.device_log_hourly\"}'\n", "labels": {"reads": [{"table": "bi.device_log_hourly", "columns": null}], "writes": [{"table": "basketball_teams", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO unesco_intangible_heritage SELECT fault_log_entry_id, savingsid, chemical, journalist_id FROM species_observations WHERE fault_log_entry_id > 289\"\n", "labels": {"reads": [{"table": "species_observations", "columns": ["fault_log_entry_id", "savingsid", "chemical", "journalist_id"]}], "writes": [{"table": "unesco_intangible_heritage", "columns": ["fault_log_entry_id", "savingsid", "chemical", "journalist_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fish_biomass SELECT preferred_foot, testtypeid, gender_code FROM public.forest_stats WHERE preferred_foot > 175\"\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": ["preferred_foot", "testtypeid", "gender_code"]}], "writes": [{"table": "fish_biomass", "columns": ["preferred_foot", "testtypeid", "gender_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewable_power\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.exposure_hourly\")\n", "labels": {"reads": [{"table": "renewable_power", "columns": null}], "writes": [{"table": "dwd.exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO campaigns SELECT * FROM legacy\nspark.sql(\"INSERT INTO department_store_chain SELECT post_id, rid, occupancy_rate FROM space_agencies_2 WHERE post_id > 81\")\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": ["post_id", "rid", "occupancy_rate"]}], "writes": [{"table": "department_store_chain", "columns": ["post_id", "rid", "occupancy_rate"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dw.users_hourly\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dw.users_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO unesco_intangible_heritage SELECT a.teamname, b.screening_id FROM fireincidents a JOIN street_markets b ON a.culturalcompetency = b.culturalcompetency\"\n", "labels": {"reads": [{"table": "fireincidents", "columns": null}, {"table": "street_markets", "columns": null}], "writes": [{"table": "unesco_intangible_heritage", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_source(ctx, \"daily_articles_by_category\")\nsave_to_target(df, \"lessons\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "daily_articles_by_category", "columns": null}], "writes": [{"table": "lessons", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO safety_incidents (arrival_date, log_entry_description) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "safety_incidents", "columns": ["arrival_date", "log_entry_description"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 98;\nSQL\n", "labels": {"reads": [{"table": "project_duration", "columns": ["winning_aircraft", "campaign_id"]}, {"table": "researcher", "columns": ["milliseconds", "part_name", "success"]}], "writes": [{"table": "sustainable_practices_2", "columns": ["milliseconds", "part_name", "success"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_sessions_df SELECT don_id, rate FROM player WHERE don_id > 277\"\n", "labels": {"reads": [{"table": "player", "columns": ["don_id", "rate"]}], "writes": [{"table": "ods.ods_sessions_df", "columns": ["don_id", "rate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bridge SELECT a.image_data, b.char_cells FROM green_buildings a JOIN savings_programs b ON a.min_salary = b.min_salary\"\n", "labels": {"reads": [{"table": "green_buildings", "columns": null}, {"table": "savings_programs", "columns": null}], "writes": [{"table": "bridge", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"subway\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "subway", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_coupon_use_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model tour_guides depends on refugees\ndbt build -s tour_guides --vars 'source: refugees'\n", "labels": {"reads": [{"table": "refugees", "columns": null}], "writes": [{"table": "tour_guides", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT decor, cultural_diversity FROM tryout\", engine)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"event\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "tryout", "columns": ["decor", "cultural_diversity"]}], "writes": [{"table": "event", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM systems\", conn)\ndf.to_sql(\"dorm_amenity\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "systems", "columns": null}], "writes": [{"table": "dorm_amenity", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO research_grants SELECT * FROM legacy\nspark.sql(\"INSERT INTO opioid_overdoses SELECT customerid, indigenous, distance, author_or_editor FROM crime_reports WHERE customerid > 18\")\n", "labels": {"reads": [{"table": "crime_reports", "columns": ["customerid", "indigenous", "distance", "author_or_editor"]}], "writes": [{"table": "opioid_overdoses", "columns": ["customerid", "indigenous", "distance", "author_or_editor"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO drama_workshop_groups SELECT ship_date, menu_id, athlete_id FROM healthcare_access_v2 WHERE ship_date > 84\")\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": ["ship_date", "menu_id", "athlete_id"]}], "writes": [{"table": "drama_workshop_groups", "columns": ["ship_date", "menu_id", "athlete_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO port_visits SELECT a.points_per_game, b.kills FROM benefits_overpayments a JOIN forests b ON a.playtime = b.playtime\"\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": null}, {"table": "forests", "columns": null}], "writes": [{"table": "port_visits", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"communication_scores\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"performingartsprograms\")\n", "labels": {"reads": [{"table": "communication_scores", "columns": null}], "writes": [{"table": "performingartsprograms", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd_risk_score_hourly\").toPandas()\ndf[[\"minesite\", \"game_genre\"]].to_sql(\"dwd.coupon_use_full\", engine, index=False)\n", "labels": {"reads": [{"table": "dwd_risk_score_hourly", "columns": null}], "writes": [{"table": "dwd.coupon_use_full", "columns": ["minesite", "game_genre"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vessel_registry\", conn)\ndf.to_sql(\"galleries\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vessel_registry", "columns": null}], "writes": [{"table": "galleries", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO threatintelligence SELECT stu_lname, flightid, capacity_percentage, longitude FROM broadband_plans WHERE stu_lname > 306\")\n", "labels": {"reads": [{"table": "broadband_plans", "columns": ["stu_lname", "flightid", "capacity_percentage", "longitude"]}], "writes": [{"table": "threatintelligence", "columns": ["stu_lname", "flightid", "capacity_percentage", "longitude"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.mission > 206).all()\n# src table: member_of\nengine.execute(\"INSERT INTO european_healthcare SELECT * FROM member_of\")\n", "labels": {"reads": [{"table": "member_of", "columns": null}], "writes": [{"table": "european_healthcare", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO virtual_tour_revenue SELECT don_id, violationid, prepnurse FROM electoral_register WHERE don_id > 304\"\n", "labels": {"reads": [{"table": "electoral_register", "columns": ["don_id", "violationid", "prepnurse"]}], "writes": [{"table": "virtual_tour_revenue", "columns": ["don_id", "violationid", "prepnurse"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT ngo_name, handling_id FROM contract_states LIMIT 387\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ads.ads_exposure_di SELECT defendant_id, dept_name, has_disability FROM oil_production WHERE defendant_id > 277\")\n", "labels": {"reads": [{"table": "contract_states", "columns": ["ngo_name", "handling_id"]}, {"table": "oil_production", "columns": ["defendant_id", "dept_name", "has_disability"]}], "writes": [{"table": "ads.ads_exposure_di", "columns": ["defendant_id", "dept_name", "has_disability"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dws.dws_cart_item_daily SELECT branch_id, regulation, eventname FROM crops WHERE branch_id > 34\"\n", "labels": {"reads": [{"table": "crops", "columns": ["branch_id", "regulation", "eventname"]}], "writes": [{"table": "dws.dws_cart_item_daily", "columns": ["branch_id", "regulation", "eventname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO recyclingrates SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model shipments_df depends on cybersecuritybudget\ndbt build -s shipments_df --vars '{\"src\":\"cybersecuritybudget\"}'\n", "labels": {"reads": [{"table": "cybersecuritybudget", "columns": null}], "writes": [{"table": "shipments_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO movie SELECT co2_reduction, individual_name, operationdate, invoice_id FROM wind_turbines WHERE co2_reduction > 226\"\n", "labels": {"reads": [{"table": "wind_turbines", "columns": ["co2_reduction", "individual_name", "operationdate", "invoice_id"]}], "writes": [{"table": "movie", "columns": ["co2_reduction", "individual_name", "operationdate", "invoice_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO nutritionfacts SELECT opid, src_apid FROM marine_life_populations WHERE opid > 493\"\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": ["opid", "src_apid"]}], "writes": [{"table": "nutritionfacts", "columns": ["opid", "src_apid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO market_share SELECT day_of_week, studio_name, sale_price FROM recycled_polyester WHERE day_of_week > 191\");\n", "labels": {"reads": [{"table": "recycled_polyester", "columns": ["day_of_week", "studio_name", "sale_price"]}], "writes": [{"table": "market_share", "columns": ["day_of_week", "studio_name", "sale_price"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO provider_training SELECT transact_date, restorative_justice FROM culturalcompetencytraining WHERE transact_date > 421\")\n", "labels": {"reads": [{"table": "culturalcompetencytraining", "columns": ["transact_date", "restorative_justice"]}], "writes": [{"table": "provider_training", "columns": ["transact_date", "restorative_justice"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM renewableprojects\", conn)\ndf.to_sql(\"ocean_floor_mapping\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "renewableprojects", "columns": null}], "writes": [{"table": "ocean_floor_mapping", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.risk_score SELECT donor, access_count, sighting_date FROM courses WHERE donor > 250\"\n", "labels": {"reads": [{"table": "courses", "columns": ["donor", "access_count", "sighting_date"]}], "writes": [{"table": "ads.risk_score", "columns": ["donor", "access_count", "sighting_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_source(ctx, \"affiliated_with\")\npersist_to_output(df, \"ads.ads_payments_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "affiliated_with", "columns": null}], "writes": [{"table": "ads.ads_payments_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT extraction_amount, did FROM mart.mart_payments_delta\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"bi.inventory_daily\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_payments_delta", "columns": ["extraction_amount", "did"]}], "writes": [{"table": "bi.inventory_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.playdate > 312).all()\n# src table: iron_ore_production\nengine.execute(\"INSERT INTO multimodal_trips SELECT * FROM iron_ore_production\")\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": null}], "writes": [{"table": "multimodal_trips", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vessel_capacity SELECT case_type, violation_id, stat_id FROM investor_activities WHERE case_type > 331\"\n", "labels": {"reads": [{"table": "investor_activities", "columns": ["case_type", "violation_id", "stat_id"]}], "writes": [{"table": "vessel_capacity", "columns": ["case_type", "violation_id", "stat_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT restaurant, home_team FROM rental LIMIT 454\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "rental", "columns": ["restaurant", "home_team"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"all_documents\").toPandas()\ndf[[\"home_team_id\", \"property_price\"]].to_sql(\"movie_financials\", engine, index=False)\n", "labels": {"reads": [{"table": "all_documents", "columns": null}], "writes": [{"table": "movie_financials", "columns": ["home_team_id", "property_price"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO building SELECT engagement_count, district, founded, pressure FROM playergamedata WHERE engagement_count > 348\")\n", "labels": {"reads": [{"table": "playergamedata", "columns": ["engagement_count", "district", "founded", "pressure"]}], "writes": [{"table": "building", "columns": ["engagement_count", "district", "founded", "pressure"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"judges\")\nsrc.write.insertInto(\"weather\", overwrite=True)\n", "labels": {"reads": [{"table": "judges", "columns": null}], "writes": [{"table": "weather", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO gamesales SELECT a.permitdate, b.dataset FROM aircraftsquadrons a JOIN team_revenue b ON a.founder_ethnicity = b.founder_ethnicity\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "aircraftsquadrons", "columns": null}, {"table": "team_revenue", "columns": null}], "writes": [{"table": "gamesales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stories\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "stories", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"fairtradefactories\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "fairtradefactories", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"bi.bi_sessions_daily\")\nsink_to_output(df, \"mart.mart_events_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.bi_sessions_daily", "columns": null}], "writes": [{"table": "mart.mart_events_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.tasktype > 387).all()\n# src table: project\nengine.execute(\"INSERT INTO apartment_buildings SELECT * FROM project\")\n", "labels": {"reads": [{"table": "project", "columns": null}], "writes": [{"table": "apartment_buildings", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO apartment_buildings SELECT surname, jobcategory, bandmate FROM laborstatistics WHERE surname > 41\"\n", "labels": {"reads": [{"table": "laborstatistics", "columns": ["surname", "jobcategory", "bandmate"]}], "writes": [{"table": "apartment_buildings", "columns": ["surname", "jobcategory", "bandmate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 374;\nSQL\n", "labels": {"reads": [{"table": "service_budget", "columns": ["common_name", "media_literacy_score"]}, {"table": "africa_projects", "columns": ["number_cities", "datetime_detention_start", "publisher"]}], "writes": [{"table": "water_sources", "columns": ["number_cities", "datetime_detention_start", "publisher"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"financial_capability_programs\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"voyages\")\n", "labels": {"reads": [{"table": "financial_capability_programs", "columns": null}], "writes": [{"table": "voyages", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO soil_moisture SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO autonomousdriving SELECT * FROM legacy\ncur.execute(\"SELECT total_distance, shipping_mode FROM thefts LIMIT 15\")\n", "labels": {"reads": [{"table": "thefts", "columns": ["total_distance", "shipping_mode"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO engineer_skills SELECT theme, mgr_start_date FROM dwd.dwd_events_delta WHERE theme > 45\")\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": ["theme", "mgr_start_date"]}], "writes": [{"table": "engineer_skills", "columns": ["theme", "mgr_start_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO container_receipts SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.launch_company > 394).all()\n# src table: higher_ed.publications\nengine.execute(\"INSERT INTO contracts SELECT * FROM higher_ed.publications\")\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": null}], "writes": [{"table": "contracts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO drug_approvals SELECT * FROM legacy\nspark.sql(\"INSERT INTO dishes SELECT operationname, mappinglength FROM albums WHERE operationname > 197\")\n", "labels": {"reads": [{"table": "albums", "columns": ["operationname", "mappinglength"]}], "writes": [{"table": "dishes", "columns": ["operationname", "mappinglength"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO species_observations SELECT a.allocation_type, b.invoice_details FROM rural_feeder_roads a JOIN recyclers b ON a.rows = b.rows\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "rural_feeder_roads", "columns": null}, {"table": "recyclers", "columns": null}], "writes": [{"table": "species_observations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.lot_details > 88).all()\n# src table: beverages\nengine.execute(\"INSERT INTO enzyme SELECT * FROM beverages\")\n", "labels": {"reads": [{"table": "beverages", "columns": null}], "writes": [{"table": "enzyme", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO maintenancerequests (product_color, playername) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "maintenancerequests", "columns": ["product_color", "playername"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"habitats\")\nsave_to_output(df, \"claims_documents\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "habitats", "columns": null}], "writes": [{"table": "claims_documents", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.employeeid > 298).all()\n# src table: ods.ods_users_daily\nengine.execute(\"INSERT INTO site SELECT * FROM ods.ods_users_daily\")\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": null}], "writes": [{"table": "site", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"containers\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "containers", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"undergoes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"appointment\")\n", "labels": {"reads": [{"table": "undergoes", "columns": null}], "writes": [{"table": "appointment", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO danceevents SELECT fault_log_entry_id, initiative_id FROM mentalhealthproviders WHERE fault_log_entry_id > 385\");\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": ["fault_log_entry_id", "initiative_id"]}], "writes": [{"table": "danceevents", "columns": ["fault_log_entry_id", "initiative_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO shared_escooters SELECT vehicle, sitename FROM athlete_stats WHERE vehicle > 146\"], check=True)\n", "labels": {"reads": [{"table": "athlete_stats", "columns": ["vehicle", "sitename"]}], "writes": [{"table": "shared_escooters", "columns": ["vehicle", "sitename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cmi_cross_references SELECT transactionid, factory_id, tonnage, courtname FROM aquaculture_farms WHERE transactionid > 278\"], check=True)\n", "labels": {"reads": [{"table": "aquaculture_farms", "columns": ["transactionid", "factory_id", "tonnage", "courtname"]}], "writes": [{"table": "cmi_cross_references", "columns": ["transactionid", "factory_id", "tonnage", "courtname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO bookings SELECT comments, personnel FROM enrollments WHERE comments > 151\")\n", "labels": {"reads": [{"table": "enrollments", "columns": ["comments", "personnel"]}], "writes": [{"table": "bookings", "columns": ["comments", "personnel"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"emerging_markets.digital_assets\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "emerging_markets.digital_assets", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_member_point_di\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"satellitedata\")\n", "labels": {"reads": [{"table": "dwd.dwd_member_point_di", "columns": null}], "writes": [{"table": "satellitedata", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"circular_supply_chain_products\")\nsrc.write.insertInto(\"store\", overwrite=True)\n", "labels": {"reads": [{"table": "circular_supply_chain_products", "columns": null}], "writes": [{"table": "store", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_tech_adoptions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hotel_tech_adoptions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 382;\nSQL\n", "labels": {"reads": [{"table": "inmates", "columns": ["store_id", "client_first_name"]}, {"table": "justice_schemas.legal_tech_providers", "columns": ["time_hour", "oil_production", "start_station_id", "disaster_id"]}], "writes": [{"table": "fleet_management", "columns": ["time_hour", "oil_production", "start_station_id", "disaster_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO org_volunteer SELECT cultural_significance, pieces, city_id, channel_code FROM arrivals WHERE cultural_significance > 118\");\n", "labels": {"reads": [{"table": "arrivals", "columns": ["cultural_significance", "pieces", "city_id", "channel_code"]}], "writes": [{"table": "org_volunteer", "columns": ["cultural_significance", "pieces", "city_id", "channel_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tourism_centers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"streams\")\n", "labels": {"reads": [{"table": "tourism_centers", "columns": null}], "writes": [{"table": "streams", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"malicious_activity\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"disease_prevalence\")\n", "labels": {"reads": [{"table": "malicious_activity", "columns": null}], "writes": [{"table": "disease_prevalence", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO stg.users SELECT machine, date_of_completion FROM gardens WHERE machine > 296\")\n", "labels": {"reads": [{"table": "gardens", "columns": ["machine", "date_of_completion"]}], "writes": [{"table": "stg.users", "columns": ["machine", "date_of_completion"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO purchase SELECT habitat_name, promotiondate, region_id, person_id FROM ads.ads_exposure_di WHERE habitat_name > 11\"], check=True)\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": ["habitat_name", "promotiondate", "region_id", "person_id"]}], "writes": [{"table": "purchase", "columns": ["habitat_name", "promotiondate", "region_id", "person_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT wage, prof_office FROM milestones LIMIT 58\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "milestones", "columns": ["wage", "prof_office"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table vehicle_safety_testing --target-dir /tmp/land\n", "labels": {"reads": [{"table": "vehicle_safety_testing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"sectors\")\nsrc.write.insertInto(\"dw.member_point_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "sectors", "columns": null}], "writes": [{"table": "dw.member_point_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT time_id, platformid FROM dws.dws_risk_score_df LIMIT 98\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "dws.dws_risk_score_df", "columns": ["time_id", "platformid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO lots SELECT materialid, cost, product_description FROM accounts WHERE materialid > 136\"\n", "labels": {"reads": [{"table": "accounts", "columns": ["materialid", "cost", "product_description"]}], "writes": [{"table": "lots", "columns": ["materialid", "cost", "product_description"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT storeid, product_id FROM policyholders LIMIT 346\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO zip_codes SELECT transaction_type, community, hireyear, followers FROM support_programs WHERE transaction_type > 175\")\n", "labels": {"reads": [{"table": "policyholders", "columns": ["storeid", "product_id"]}, {"table": "support_programs", "columns": ["transaction_type", "community", "hireyear", "followers"]}], "writes": [{"table": "zip_codes", "columns": ["transaction_type", "community", "hireyear", "followers"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT opname, has_aloe_vera FROM ocean_species LIMIT 159\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO dwd.dwd_events_delta SELECT factory_id, numcases FROM workouts WHERE factory_id > 436\")\n", "labels": {"reads": [{"table": "ocean_species", "columns": ["opname", "has_aloe_vera"]}, {"table": "workouts", "columns": ["factory_id", "numcases"]}], "writes": [{"table": "dwd.dwd_events_delta", "columns": ["factory_id", "numcases"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO market_trends SELECT content_type, pcp, programtype, policy_type FROM mart.mart_coupon_use_df WHERE content_type > 392\"], check=True)\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_df", "columns": ["content_type", "pcp", "programtype", "policy_type"]}], "writes": [{"table": "market_trends", "columns": ["content_type", "pcp", "programtype", "policy_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO labor_productivity SELECT songname, graduate, forest_id, age_group FROM seasonalvegetables WHERE songname > 126\")\n", "labels": {"reads": [{"table": "seasonalvegetables", "columns": ["songname", "graduate", "forest_id", "age_group"]}], "writes": [{"table": "labor_productivity", "columns": ["songname", "graduate", "forest_id", "age_group"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.change_date > 356).all()\n# src table: sector_incidents\nengine.execute(\"INSERT INTO document_locations SELECT * FROM sector_incidents\")\n", "labels": {"reads": [{"table": "sector_incidents", "columns": null}], "writes": [{"table": "document_locations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"militarypatents\").where(\"dt = current_date()\").writeTo(\"retailerg\").append()\n", "labels": {"reads": [{"table": "militarypatents", "columns": null}], "writes": [{"table": "retailerg", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dw_users_full SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO militarypersonnel SELECT end_speed, campus, player_name, health_equity_metric_1 FROM organic_cosmetics WHERE end_speed > 102\")\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": ["end_speed", "campus", "player_name", "health_equity_metric_1"]}], "writes": [{"table": "militarypersonnel", "columns": ["end_speed", "campus", "player_name", "health_equity_metric_1"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO safetytestingcounts SELECT researcher_name, catalog_entry_id FROM eventlocations WHERE researcher_name > 233\")\n", "labels": {"reads": [{"table": "eventlocations", "columns": ["researcher_name", "catalog_entry_id"]}], "writes": [{"table": "safetytestingcounts", "columns": ["researcher_name", "catalog_entry_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 129;\nEOF\n", "labels": {"reads": [{"table": "plays_games", "columns": ["productiondate", "reaction_time", "operationname"]}], "writes": [{"table": "space_debris", "columns": ["productiondate", "reaction_time", "operationname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO marathons (session_date, financially_capable) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "marathons", "columns": ["session_date", "financially_capable"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"communitypolicing\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "communitypolicing", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.transaction_type_description > 5).all()\n# src table: building_stats\nengine.execute(\"INSERT INTO first_notification_of_loss SELECT * FROM building_stats\")\n", "labels": {"reads": [{"table": "building_stats", "columns": null}], "writes": [{"table": "first_notification_of_loss", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO satellitedata SELECT distance, campaign_name, hours_developed FROM warehouses WHERE distance > 32\");\n", "labels": {"reads": [{"table": "warehouses", "columns": ["distance", "campaign_name", "hours_developed"]}], "writes": [{"table": "satellitedata", "columns": ["distance", "campaign_name", "hours_developed"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT volume, market FROM donationprograms\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"dwd.exposure_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "donationprograms", "columns": ["volume", "market"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM nasa_mars_program\"\n", "labels": {"reads": [{"table": "nasa_mars_program", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"rehab_centers\");\ndf.write().mode(\"overwrite\").saveAsTable(\"list\");\n", "labels": {"reads": [{"table": "rehab_centers", "columns": null}], "writes": [{"table": "list", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.response_received_date > 462).all()\n# src table: product_revenue\nengine.execute(\"INSERT INTO rnd_budget SELECT * FROM product_revenue\")\n", "labels": {"reads": [{"table": "product_revenue", "columns": null}], "writes": [{"table": "rnd_budget", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"emergencies\")\nsrc.write.insertInto(\"architect\", overwrite=True)\n", "labels": {"reads": [{"table": "emergencies", "columns": null}], "writes": [{"table": "architect", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ingredients SELECT * FROM legacy\nspark.sql(\"INSERT INTO subway_stations_seoul SELECT airline, roomname, investorgender, billingid FROM draft_copies WHERE airline > 387\")\n", "labels": {"reads": [{"table": "draft_copies", "columns": ["airline", "roomname", "investorgender", "billingid"]}], "writes": [{"table": "subway_stations_seoul", "columns": ["airline", "roomname", "investorgender", "billingid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"team_members\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ads\")\n", "labels": {"reads": [{"table": "team_members", "columns": null}], "writes": [{"table": "ads", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart.mart_refunds_di SELECT release_year, booking_status_code FROM savings WHERE release_year > 38\"\n", "labels": {"reads": [{"table": "savings", "columns": ["release_year", "booking_status_code"]}], "writes": [{"table": "mart.mart_refunds_di", "columns": ["release_year", "booking_status_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"legalaidrequests\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "legalaidrequests", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM attorney_billing_rates\"\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO dorm_amenity SELECT a.createdate, b.caseid FROM rehab_centers a JOIN wedding b ON a.duration = b.duration\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "rehab_centers", "columns": null}, {"table": "wedding", "columns": null}], "writes": [{"table": "dorm_amenity", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cell_mobile_phone_number, case_burden FROM job_change LIMIT 486\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO militaryinnovations SELECT esg_factor, created_date, participant_count, birth_date FROM coral_reefs WHERE esg_factor > 343\")\n", "labels": {"reads": [{"table": "job_change", "columns": ["cell_mobile_phone_number", "case_burden"]}, {"table": "coral_reefs", "columns": ["esg_factor", "created_date", "participant_count", "birth_date"]}], "writes": [{"table": "militaryinnovations", "columns": ["esg_factor", "created_date", "participant_count", "birth_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"initiatives\").where(\"dt = current_date()\").writeTo(\"investmentsesg\").append()\n", "labels": {"reads": [{"table": "initiatives", "columns": null}], "writes": [{"table": "investmentsesg", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"venture\")\nsrc.write.insertInto(\"water_conservation_brazil\", overwrite=True)\n", "labels": {"reads": [{"table": "venture", "columns": null}], "writes": [{"table": "water_conservation_brazil", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.ads_refunds_hourly SELECT donortype, teamid FROM dw.dw_sessions_delta WHERE donortype > 209\"], check=True)\n", "labels": {"reads": [{"table": "dw.dw_sessions_delta", "columns": ["donortype", "teamid"]}], "writes": [{"table": "ads.ads_refunds_hourly", "columns": ["donortype", "teamid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table bi.bi_events_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table crime_incidents --columns classtype,sale_volume --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "crime_incidents", "columns": ["classtype", "sale_volume"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO civilcases SELECT * FROM legacy\nspark.sql(\"INSERT INTO tickets_3 SELECT fundingid, shop_details, district_name FROM tokyo_motor_show WHERE fundingid > 369\")\n", "labels": {"reads": [{"table": "tokyo_motor_show", "columns": ["fundingid", "shop_details", "district_name"]}], "writes": [{"table": "tickets_3", "columns": ["fundingid", "shop_details", "district_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO artsheritage SELECT capacity_mw, habitat_id FROM ods.ods_risk_score_df WHERE capacity_mw > 309\"], check=True)\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": ["capacity_mw", "habitat_id"]}], "writes": [{"table": "artsheritage", "columns": ["capacity_mw", "habitat_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO publication SELECT a.studentname, b.petid FROM accelerator_compatible_browser a JOIN ethics_violations b ON a.death_year = b.death_year\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "accelerator_compatible_browser", "columns": null}, {"table": "ethics_violations", "columns": null}], "writes": [{"table": "publication", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fieldd_info SELECT a.invested, b.founder_lgbtq FROM scores a JOIN program_outcomes b ON a.player_api_id = b.player_api_id\"\n", "labels": {"reads": [{"table": "scores", "columns": null}, {"table": "program_outcomes", "columns": null}], "writes": [{"table": "fieldd_info", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO channel SELECT model_name, prof_num, energy_production, pilot_name FROM rural_resources WHERE model_name > 4\")\n", "labels": {"reads": [{"table": "rural_resources", "columns": ["model_name", "prof_num", "energy_production", "pilot_name"]}], "writes": [{"table": "channel", "columns": ["model_name", "prof_num", "energy_production", "pilot_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd.dwd_orders_daily\");\ndf.write().mode(\"overwrite\").saveAsTable(\"projects\");\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": null}], "writes": [{"table": "projects", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM militaryequipmentsales\"\n", "labels": {"reads": [{"table": "militaryequipmentsales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO screen_mode SELECT offer_id, servicename, completion_status, record_id FROM satellitematerials WHERE offer_id > 365\")\n", "labels": {"reads": [{"table": "satellitematerials", "columns": ["offer_id", "servicename", "completion_status", "record_id"]}], "writes": [{"table": "screen_mode", "columns": ["offer_id", "servicename", "completion_status", "record_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table pets --columns reports_to,clubid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "pets", "columns": ["reports_to", "clubid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO constructorstandings SELECT * FROM legacy\nspark.sql(\"INSERT INTO shark_biomass SELECT services, inspection_id FROM cotton_source WHERE services > 65\")\n", "labels": {"reads": [{"table": "cotton_source", "columns": ["services", "inspection_id"]}], "writes": [{"table": "shark_biomass", "columns": ["services", "inspection_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.user_rating > 84).all()\n# src table: militaryinnovations\nengine.execute(\"INSERT INTO dws.coupon_use_di SELECT * FROM militaryinnovations\")\n", "labels": {"reads": [{"table": "militaryinnovations", "columns": null}], "writes": [{"table": "dws.coupon_use_di", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO course SELECT account_name, rate, enrollment_date FROM auto_show WHERE account_name > 75\"\n", "labels": {"reads": [{"table": "auto_show", "columns": ["account_name", "rate", "enrollment_date"]}], "writes": [{"table": "course", "columns": ["account_name", "rate", "enrollment_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.campaigns_df SELECT postal_code, sustainability_id, artifacttype, employment_id FROM browser WHERE postal_code > 373\"], check=True)\n", "labels": {"reads": [{"table": "browser", "columns": ["postal_code", "sustainability_id", "artifacttype", "employment_id"]}], "writes": [{"table": "stg.campaigns_df", "columns": ["postal_code", "sustainability_id", "artifacttype", "employment_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO rd_expenditure SELECT policy, eventid, alid FROM community_health_centers WHERE policy > 75\");\n", "labels": {"reads": [{"table": "community_health_centers", "columns": ["policy", "eventid", "alid"]}], "writes": [{"table": "rd_expenditure", "columns": ["policy", "eventid", "alid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"social_good_education\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"safety_testing\")\n", "labels": {"reads": [{"table": "social_good_education", "columns": null}], "writes": [{"table": "safety_testing", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.vendors_full (quality_rank, rental_rate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.vendors_full", "columns": ["quality_rank", "rental_rate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.orders_daily\"\n", "labels": {"reads": [{"table": "stg.orders_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO mart.mart_payments_hourly SELECT garment, spacecraft_model, port_id FROM safety_incidents_india WHERE garment > 358\"\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": ["garment", "spacecraft_model", "port_id"]}], "writes": [{"table": "mart.mart_payments_hourly", "columns": ["garment", "spacecraft_model", "port_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table legal_aid_providers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "legal_aid_providers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"field5\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "field5", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO taj_mahal_visitors SELECT indigenous, wellname FROM fields WHERE indigenous > 276\")\n", "labels": {"reads": [{"table": "fields", "columns": ["indigenous", "wellname"]}], "writes": [{"table": "taj_mahal_visitors", "columns": ["indigenous", "wellname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO artsandcrafts SELECT budget_type_description, connection, eventname, filingdate FROM flu_cases WHERE budget_type_description > 447\")\n", "labels": {"reads": [{"table": "flu_cases", "columns": ["budget_type_description", "connection", "eventname", "filingdate"]}], "writes": [{"table": "artsandcrafts", "columns": ["budget_type_description", "connection", "eventname", "filingdate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO genderdistribution SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO player_coach SELECT mouse_id, text, attack_count FROM ticketsales WHERE mouse_id > 127\"\n", "labels": {"reads": [{"table": "ticketsales", "columns": ["mouse_id", "text", "attack_count"]}], "writes": [{"table": "player_coach", "columns": ["mouse_id", "text", "attack_count"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO haircaresales SELECT minesite, hotel_id FROM publications WHERE minesite > 106\")\n", "labels": {"reads": [{"table": "publications", "columns": ["minesite", "hotel_id"]}], "writes": [{"table": "haircaresales", "columns": ["minesite", "hotel_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 323;\nSQL\n", "labels": {"reads": [{"table": "crimes", "columns": ["studentname", "attendance_date"]}, {"table": "race", "columns": ["water_temp", "affirmative", "party_name"]}], "writes": [{"table": "exhibition_artworks", "columns": ["water_temp", "affirmative", "party_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"auto_shows\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artworksales\")\n", "labels": {"reads": [{"table": "auto_shows", "columns": null}], "writes": [{"table": "artworksales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO singer SELECT a.gender_group, b.num_hotels FROM food_justice_orgs a JOIN government_funding b ON a.player_id = b.player_id\"\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": null}, {"table": "government_funding", "columns": null}], "writes": [{"table": "singer", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"hotel_business_partnerships\");\ndf.write().mode(\"overwrite\").saveAsTable(\"florida_conservation_initiatives\");\n", "labels": {"reads": [{"table": "hotel_business_partnerships", "columns": null}], "writes": [{"table": "florida_conservation_initiatives", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO foodsafetyrecords SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT player_id, skill_id FROM clothingsales LIMIT 470\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "clothingsales", "columns": ["player_id", "skill_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table yoga --columns worker_count,healthcareid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "yoga", "columns": ["worker_count", "healthcareid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"disease_prevalence\").toPandas()\ndf[[\"recordid\", \"payment_id\"]].to_sql(\"shark_biomass\", engine, index=False)\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": null}], "writes": [{"table": "shark_biomass", "columns": ["recordid", "payment_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart_campaigns_delta SELECT * FROM legacy\ncur.execute(\"SELECT team_id, nutrient_level FROM training LIMIT 61\")\n", "labels": {"reads": [{"table": "training", "columns": ["team_id", "nutrient_level"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.player > 13).all()\n# src table: crop_temperature\nengine.execute(\"INSERT INTO astronaut_medical_3 SELECT * FROM crop_temperature\")\n", "labels": {"reads": [{"table": "crop_temperature", "columns": null}], "writes": [{"table": "astronaut_medical_3", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"intelligence_agents\")\nsave_to_warehouse(df, \"flight_safety\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": null}], "writes": [{"table": "flight_safety", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO community_engagement SELECT supporter, building_id FROM agriculturalinnovations WHERE supporter > 358\")\n", "labels": {"reads": [{"table": "agriculturalinnovations", "columns": ["supporter", "building_id"]}], "writes": [{"table": "community_engagement", "columns": ["supporter", "building_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi_refunds_daily SELECT ai_customer_service, market_rate, product_details, contract_type FROM italy_culture WHERE ai_customer_service > 96\"], check=True)\n", "labels": {"reads": [{"table": "italy_culture", "columns": ["ai_customer_service", "market_rate", "product_details", "contract_type"]}], "writes": [{"table": "bi_refunds_daily", "columns": ["ai_customer_service", "market_rate", "product_details", "contract_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 442;\nEOF\n", "labels": {"reads": [{"table": "initiatives_3", "columns": ["wellbeing_score", "hospital_name"]}], "writes": [{"table": "battery_projects", "columns": ["wellbeing_score", "hospital_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM customer_master_index\", conn)\ndf.to_sql(\"training_programs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "customer_master_index", "columns": null}], "writes": [{"table": "training_programs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"transportation\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"visits\")\n", "labels": {"reads": [{"table": "transportation", "columns": null}], "writes": [{"table": "visits", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"acceptance\")\nexport_to_sink(df, \"ports\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "acceptance", "columns": null}], "writes": [{"table": "ports", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mineral_extraction_us\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mineral_extraction_us", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"repair_assignment\").toPandas()\ndf[[\"town_city\", \"share_in_percent\"]].to_sql(\"bi_products\", engine, index=False)\n", "labels": {"reads": [{"table": "repair_assignment", "columns": null}], "writes": [{"table": "bi_products", "columns": ["town_city", "share_in_percent"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mental_health_parity_violations (total, restorative_justice) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mental_health_parity_violations", "columns": ["total", "restorative_justice"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.device_log_df SELECT genre_id, foreign FROM chemicalproducts WHERE genre_id > 370\"\n", "labels": {"reads": [{"table": "chemicalproducts", "columns": ["genre_id", "foreign"]}], "writes": [{"table": "stg.device_log_df", "columns": ["genre_id", "foreign"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO item SELECT problem_log_id, supplier, width FROM student_course_registrations WHERE problem_log_id > 282\"\n", "labels": {"reads": [{"table": "student_course_registrations", "columns": ["problem_log_id", "supplier", "width"]}], "writes": [{"table": "item", "columns": ["problem_log_id", "supplier", "width"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"aid_missions\").where(\"dt = current_date()\").writeTo(\"arcticocean\").append()\n", "labels": {"reads": [{"table": "aid_missions", "columns": null}], "writes": [{"table": "arcticocean", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO military_expenditure SELECT * FROM legacy\ncur.execute(\"SELECT average_attendance, arrival FROM military_expenditure LIMIT 487\")\n", "labels": {"reads": [{"table": "military_expenditure", "columns": ["average_attendance", "arrival"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.trainingid > 51).all()\n# src table: architect\nengine.execute(\"INSERT INTO community_development.transactions SELECT * FROM architect\")\n", "labels": {"reads": [{"table": "architect", "columns": null}], "writes": [{"table": "community_development.transactions", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"chip_model\")\nsrc.write.insertInto(\"party_services\", overwrite=True)\n", "labels": {"reads": [{"table": "chip_model", "columns": null}], "writes": [{"table": "party_services", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"arctic_research\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "arctic_research", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"labor_practices\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "labor_practices", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.mart_refunds_hourly\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mart.mart_refunds_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sourcing SELECT a.address_line_2, b.project_name FROM foodaid a JOIN safe_dataset b ON a.customer_email_address = b.customer_email_address\"\n", "labels": {"reads": [{"table": "foodaid", "columns": null}, {"table": "safe_dataset", "columns": null}], "writes": [{"table": "sourcing", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"patient_satisfaction\");\ndf.write().mode(\"overwrite\").saveAsTable(\"payments\");\n", "labels": {"reads": [{"table": "patient_satisfaction", "columns": null}], "writes": [{"table": "payments", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 190;\nEOF\n", "labels": {"reads": [{"table": "safety_research", "columns": ["contract_start", "billing_country", "material", "date_of_latest_revision"]}], "writes": [{"table": "accounts", "columns": ["contract_start", "billing_country", "material", "date_of_latest_revision"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mars_missions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mars_missions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"volunteerprograms\")\nsrc.write.insertInto(\"student\", overwrite=True)\n", "labels": {"reads": [{"table": "volunteerprograms", "columns": null}], "writes": [{"table": "student", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stories SELECT * FROM legacy\ncur.execute(\"SELECT building_manager, fueldate FROM dwd.dwd_users_hourly LIMIT 251\")\n", "labels": {"reads": [{"table": "dwd.dwd_users_hourly", "columns": ["building_manager", "fueldate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.ods_events_daily\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"bi.inventory_daily\")\n", "labels": {"reads": [{"table": "ods.ods_events_daily", "columns": null}], "writes": [{"table": "bi.inventory_daily", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO public_works_projects SELECT model_id, official_name, experienceid FROM station_company WHERE model_id > 16\");\n", "labels": {"reads": [{"table": "station_company", "columns": ["model_id", "official_name", "experienceid"]}], "writes": [{"table": "public_works_projects", "columns": ["model_id", "official_name", "experienceid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO track (workoutname, people_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "track", "columns": ["workoutname", "people_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stg.stg_users_full (class_room, fleet_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_users_full", "columns": ["class_room", "fleet_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO animal_species SELECT labor_id, update_date FROM pollution_control_initiatives WHERE labor_id > 40\"\n", "labels": {"reads": [{"table": "pollution_control_initiatives", "columns": ["labor_id", "update_date"]}], "writes": [{"table": "animal_species", "columns": ["labor_id", "update_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table workforce --target-dir /tmp/land\n", "labels": {"reads": [{"table": "workforce", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO platformstats SELECT worker_id, unit_name FROM dws_clicks_di WHERE worker_id > 447\"], check=True)\n", "labels": {"reads": [{"table": "dws_clicks_di", "columns": ["worker_id", "unit_name"]}], "writes": [{"table": "platformstats", "columns": ["worker_id", "unit_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO projecttimeline (creationyear, customer_code) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "projecttimeline", "columns": ["creationyear", "customer_code"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO climateresearch SELECT payment_method_code, coalid, commodity FROM space_telescopes WHERE payment_method_code > 366\"\n", "labels": {"reads": [{"table": "space_telescopes", "columns": ["payment_method_code", "coalid", "commodity"]}], "writes": [{"table": "climateresearch", "columns": ["payment_method_code", "coalid", "commodity"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"ads.ads_orders_full\")\nupsert_to_sink(df, \"farms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.ads_orders_full", "columns": null}], "writes": [{"table": "farms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ods_payments_delta SELECT section_title, number_of_matches, strategy_name, payment_method FROM spacecraftmanufacturing WHERE section_title > 462\"], check=True)\n", "labels": {"reads": [{"table": "spacecraftmanufacturing", "columns": ["section_title", "number_of_matches", "strategy_name", "payment_method"]}], "writes": [{"table": "ods_payments_delta", "columns": ["section_title", "number_of_matches", "strategy_name", "payment_method"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO sustainableproduction SELECT amount_settled, railway_id, testdate, custid FROM healthcare_facilities WHERE amount_settled > 487\"], check=True)\n", "labels": {"reads": [{"table": "healthcare_facilities", "columns": ["amount_settled", "railway_id", "testdate", "custid"]}], "writes": [{"table": "sustainableproduction", "columns": ["amount_settled", "railway_id", "testdate", "custid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cultivators (dataset, energy_source) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cultivators", "columns": ["dataset", "energy_source"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tree_types\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"travel_advisory\")\n", "labels": {"reads": [{"table": "tree_types", "columns": null}], "writes": [{"table": "travel_advisory", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT shop_id, time_minute FROM home_game LIMIT 70\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "home_game", "columns": ["shop_id", "time_minute"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"reo_production\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "reo_production", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO offender_demographics SELECT * FROM legacy\ncur.execute(\"SELECT graphics_mode, build_date FROM school_bus LIMIT 447\")\n", "labels": {"reads": [{"table": "school_bus", "columns": ["graphics_mode", "build_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"irrigation_systems\").toPandas()\ndf[[\"fouls\", \"number_thousands\"]].to_sql(\"mission\", engine, index=False)\n", "labels": {"reads": [{"table": "irrigation_systems", "columns": null}], "writes": [{"table": "mission", "columns": ["fouls", "number_thousands"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model user_profiles depends on product_characteristics\ndbt run -s user_profiles --vars '{\"src\":\"product_characteristics\"}'\n", "labels": {"reads": [{"table": "product_characteristics", "columns": null}], "writes": [{"table": "user_profiles", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM appellations\", conn)\ndf.to_sql(\"project_issues\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "appellations", "columns": null}], "writes": [{"table": "project_issues", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT diet, college_location FROM satellites_in_orbit LIMIT 27\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "satellites_in_orbit", "columns": ["diet", "college_location"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donationhistory\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ocean_acidification_antarctic\")\n", "labels": {"reads": [{"table": "donationhistory", "columns": null}], "writes": [{"table": "ocean_acidification_antarctic", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table expenditure --target-dir /tmp/land\n", "labels": {"reads": [{"table": "expenditure", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO platformh SELECT a.truck_licence_number, b.fleet_series FROM branch a JOIN network_infrastructure b ON a.artifactname = b.artifactname\"\n", "labels": {"reads": [{"table": "branch", "columns": null}, {"table": "network_infrastructure", "columns": null}], "writes": [{"table": "platformh", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO art SELECT a.branch_id, b.tour_id FROM organicproducts a JOIN developers b ON a.archaeologist_id = b.archaeologist_id\"\n", "labels": {"reads": [{"table": "organicproducts", "columns": null}, {"table": "developers", "columns": null}], "writes": [{"table": "art", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT resolved, institution_name FROM total_consumption\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"platformstats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "total_consumption", "columns": ["resolved", "institution_name"]}], "writes": [{"table": "platformstats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO carbon_prices SELECT cinema_id, password, parameters, media_literacy_score FROM purchases WHERE cinema_id > 460\"\n", "labels": {"reads": [{"table": "purchases", "columns": ["cinema_id", "password", "parameters", "media_literacy_score"]}], "writes": [{"table": "carbon_prices", "columns": ["cinema_id", "password", "parameters", "media_literacy_score"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ads_sessions_di depends on certificate\ndbt build --select ads_sessions_di --vars '{\"source_table\":\"certificate\"}'\n", "labels": {"reads": [{"table": "certificate", "columns": null}], "writes": [{"table": "ads_sessions_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO assessment_notes SELECT highscore, account_name, product_size, max_wind_speed_mph FROM threat_intelligence WHERE highscore > 183\"\n", "labels": {"reads": [{"table": "threat_intelligence", "columns": ["highscore", "account_name", "product_size", "max_wind_speed_mph"]}], "writes": [{"table": "assessment_notes", "columns": ["highscore", "account_name", "product_size", "max_wind_speed_mph"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_table(ctx, \"graduates\")\nexport_to_store(df, \"environmentalimpact\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "graduates", "columns": null}], "writes": [{"table": "environmentalimpact", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 387;\nEOF\n", "labels": {"reads": [{"table": "member_of", "columns": ["transaction_amount", "disaster_type", "population"]}], "writes": [{"table": "thefts", "columns": ["transaction_amount", "disaster_type", "population"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT other_hotel_details, song_id FROM athlete_wellbeing\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"ota_revenue\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": ["other_hotel_details", "song_id"]}], "writes": [{"table": "ota_revenue", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"financialwellbeing\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "financialwellbeing", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO safety_incident SELECT a.partitionid, b.labordate FROM ai_safety_incidents a JOIN ods.ods_users_daily b ON a.gender_mf = b.gender_mf\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": null}, {"table": "ods.ods_users_daily", "columns": null}], "writes": [{"table": "safety_incident", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO department_stores SELECT a.production_bopd, b.funding_year FROM total_consumption a JOIN organicproducts b ON a.join_year = b.join_year\"\n", "labels": {"reads": [{"table": "total_consumption", "columns": null}, {"table": "organicproducts", "columns": null}], "writes": [{"table": "department_stores", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO studies SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT workouttype, restaurant_name FROM agriculturalinnovations LIMIT 17\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO student_lifelong_learning SELECT chemical_id, duration, art, development_type FROM eventparticipation WHERE chemical_id > 6\")\n", "labels": {"reads": [{"table": "agriculturalinnovations", "columns": ["workouttype", "restaurant_name"]}, {"table": "eventparticipation", "columns": ["chemical_id", "duration", "art", "development_type"]}], "writes": [{"table": "student_lifelong_learning", "columns": ["chemical_id", "duration", "art", "development_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"apac_hotel_views\")\ndump_to_output(df, \"sustainableprojects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": null}], "writes": [{"table": "sustainableprojects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"course_attendance\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "course_attendance", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT operating_system, biomass FROM rare_earth_companies LIMIT 180\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO water_distribution SELECT name, handling_date FROM mobile_customers_global WHERE name > 188\")\n", "labels": {"reads": [{"table": "rare_earth_companies", "columns": ["operating_system", "biomass"]}, {"table": "mobile_customers_global", "columns": ["name", "handling_date"]}], "writes": [{"table": "water_distribution", "columns": ["name", "handling_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vendor_state, base_name FROM researchpapers\", engine)\nimport logging\ndf.to_sql(\"solar_energy\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "researchpapers", "columns": ["vendor_state", "base_name"]}], "writes": [{"table": "solar_energy", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO financial_capability_program SELECT quarter, method_name, fan_age, expedition_name FROM dws.dws_campaigns_df WHERE quarter > 327\"\n", "labels": {"reads": [{"table": "dws.dws_campaigns_df", "columns": ["quarter", "method_name", "fan_age", "expedition_name"]}], "writes": [{"table": "financial_capability_program", "columns": ["quarter", "method_name", "fan_age", "expedition_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart_exposure_hourly SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO dws.coupon_use_di SELECT sensor_id, working_horses, emp_num FROM dwd_payments_delta WHERE sensor_id > 385\")\n", "labels": {"reads": [{"table": "dwd_payments_delta", "columns": ["sensor_id", "working_horses", "emp_num"]}], "writes": [{"table": "dws.coupon_use_di", "columns": ["sensor_id", "working_horses", "emp_num"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT artworkyear, participant_details FROM asteroids\", engine)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"producesupplier\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "asteroids", "columns": ["artworkyear", "participant_details"]}], "writes": [{"table": "producesupplier", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO recycling_rates_state SELECT sighting_id, recruitername, genre_is FROM mobile_plans WHERE sighting_id > 441\"\n", "labels": {"reads": [{"table": "mobile_plans", "columns": ["sighting_id", "recruitername", "genre_is"]}], "writes": [{"table": "recycling_rates_state", "columns": ["sighting_id", "recruitername", "genre_is"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM sales_by_quarter\"\n", "labels": {"reads": [{"table": "sales_by_quarter", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO songs_length SELECT producerid, detection_date FROM orders WHERE producerid > 141\");\n", "labels": {"reads": [{"table": "orders", "columns": ["producerid", "detection_date"]}], "writes": [{"table": "songs_length", "columns": ["producerid", "detection_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO clinics SELECT savings, machinery_id, citizen_id, id FROM adaptation_projects WHERE savings > 356\"\n", "labels": {"reads": [{"table": "adaptation_projects", "columns": ["savings", "machinery_id", "citizen_id", "id"]}], "writes": [{"table": "clinics", "columns": ["savings", "machinery_id", "citizen_id", "id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tourist_attraction_features\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cosmetics\")\n", "labels": {"reads": [{"table": "tourist_attraction_features", "columns": null}], "writes": [{"table": "cosmetics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ads.ads_device_log_di SELECT * FROM legacy\nspark.sql(\"INSERT INTO state_usage SELECT energy_generated, sponsor_name, individual_middle_name FROM continents WHERE energy_generated > 215\")\n", "labels": {"reads": [{"table": "continents", "columns": ["energy_generated", "sponsor_name", "individual_middle_name"]}], "writes": [{"table": "state_usage", "columns": ["energy_generated", "sponsor_name", "individual_middle_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table agricultural_projects --columns fault_short_name,discount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "agricultural_projects", "columns": ["fault_short_name", "discount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.member_point_df\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tokyo_water_consumption\")\n", "labels": {"reads": [{"table": "ods.member_point_df", "columns": null}], "writes": [{"table": "tokyo_water_consumption", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO emergencies SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT condition_id, jobcategory FROM diversification_projects LIMIT 487\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "diversification_projects", "columns": ["condition_id", "jobcategory"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"circular_economy\")\nsrc.write.insertInto(\"genetic.projects\", overwrite=True)\n", "labels": {"reads": [{"table": "circular_economy", "columns": null}], "writes": [{"table": "genetic.projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO ods.vendors_di SELECT issues, zipcode FROM news_stories WHERE issues > 114\"\n", "labels": {"reads": [{"table": "news_stories", "columns": ["issues", "zipcode"]}], "writes": [{"table": "ods.vendors_di", "columns": ["issues", "zipcode"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.bi_payments SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"salesperson\").where(\"dt = current_date()\").writeTo(\"healthcare_system\").append()\n", "labels": {"reads": [{"table": "salesperson", "columns": null}], "writes": [{"table": "healthcare_system", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO open_pedagogy_exam SELECT cultural_significance, trip_end_time, garment_id FROM scientists WHERE cultural_significance > 156\"\n", "labels": {"reads": [{"table": "scientists", "columns": ["cultural_significance", "trip_end_time", "garment_id"]}], "writes": [{"table": "open_pedagogy_exam", "columns": ["cultural_significance", "trip_end_time", "garment_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO microfinance_clients SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"public.collected_fare\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "public.collected_fare", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO emissions SELECT visitor_id, org_name, max_gust_speed_mph, change_date FROM stories WHERE visitor_id > 124\")\n", "labels": {"reads": [{"table": "stories", "columns": ["visitor_id", "org_name", "max_gust_speed_mph", "change_date"]}], "writes": [{"table": "emissions", "columns": ["visitor_id", "org_name", "max_gust_speed_mph", "change_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ticketspending\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ticketspending", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads_exposure_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"customers_policies\")\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": null}], "writes": [{"table": "customers_policies", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"infection_rates\").where(\"dt = current_date()\").writeTo(\"public_transportation_routes\").append()\n", "labels": {"reads": [{"table": "infection_rates", "columns": null}], "writes": [{"table": "public_transportation_routes", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"people_addresses\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"communityhealthworkerscanada\")\n", "labels": {"reads": [{"table": "people_addresses", "columns": null}], "writes": [{"table": "communityhealthworkerscanada", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 290;\nSQL\n", "labels": {"reads": [{"table": "contracts", "columns": ["orderid", "stu_gpa"]}, {"table": "mart.shipments_df", "columns": ["mental_health_rating", "vendor_name"]}], "writes": [{"table": "staff", "columns": ["mental_health_rating", "vendor_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vehicle, resource_type FROM product_reviews\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ndf.to_sql(\"dwd.dwd_member_point_full\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "product_reviews", "columns": ["vehicle", "resource_type"]}], "writes": [{"table": "dwd.dwd_member_point_full", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"defenseprojects\")\npersist_to_output(df, \"education_union\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "defenseprojects", "columns": null}], "writes": [{"table": "education_union", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO production_yearly SELECT tank, budget_allocated, day_number, visitor_count FROM sustainable_practices WHERE tank > 330\")\n", "labels": {"reads": [{"table": "sustainable_practices", "columns": ["tank", "budget_allocated", "day_number", "visitor_count"]}], "writes": [{"table": "production_yearly", "columns": ["tank", "budget_allocated", "day_number", "visitor_count"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.flno > 105).all()\n# src table: weather\nengine.execute(\"INSERT INTO playergamehistory SELECT * FROM weather\")\n", "labels": {"reads": [{"table": "weather", "columns": null}], "writes": [{"table": "playergamehistory", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO military_innovation (observation_id, last_service) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "military_innovation", "columns": ["observation_id", "last_service"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.node_id > 200).all()\n# src table: season_assists\nengine.execute(\"INSERT INTO program_budget SELECT * FROM season_assists\")\n", "labels": {"reads": [{"table": "season_assists", "columns": null}], "writes": [{"table": "program_budget", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT labor_hour_id, extraction_date FROM public.ev_sales LIMIT 408\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "public.ev_sales", "columns": ["labor_hour_id", "extraction_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO lessons SELECT hoursspent, part_name, brandid FROM australian_states WHERE hoursspent > 34\"\n", "labels": {"reads": [{"table": "australian_states", "columns": ["hoursspent", "part_name", "brandid"]}], "writes": [{"table": "lessons", "columns": ["hoursspent", "part_name", "brandid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model workersalaries depends on communitycenters\ndbt build -s workersalaries --vars '{\"src\":\"communitycenters\"}'\n", "labels": {"reads": [{"table": "communitycenters", "columns": null}], "writes": [{"table": "workersalaries", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO flights SELECT dormid, jobtitle, comment_count FROM incidents_by_month WHERE dormid > 386\"\n", "labels": {"reads": [{"table": "incidents_by_month", "columns": ["dormid", "jobtitle", "comment_count"]}], "writes": [{"table": "flights", "columns": ["dormid", "jobtitle", "comment_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM community.donations\"\n", "labels": {"reads": [{"table": "community.donations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"tourist_destinations\");\ndf.write().mode(\"overwrite\").saveAsTable(\"player_award\");\n", "labels": {"reads": [{"table": "tourist_destinations", "columns": null}], "writes": [{"table": "player_award", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"waste_generation\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "waste_generation", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table tech_volunteers --columns financing_date,assessmentid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "tech_volunteers", "columns": ["financing_date", "assessmentid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT built_year, genre_is FROM submersible_dives LIMIT 28\")\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO mining_operations SELECT strategy_name, pilot_id FROM plants WHERE strategy_name > 69\")\n", "labels": {"reads": [{"table": "submersible_dives", "columns": ["built_year", "genre_is"]}, {"table": "plants", "columns": ["strategy_name", "pilot_id"]}], "writes": [{"table": "mining_operations", "columns": ["strategy_name", "pilot_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"document_sections_images\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "document_sections_images", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table incarcerated --columns catalog_id,decor --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "incarcerated", "columns": ["catalog_id", "decor"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws_cart_item\", conn)\ndf.to_sql(\"astronautmedicaldata\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws_cart_item", "columns": null}], "writes": [{"table": "astronautmedicaldata", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 257;\nSQL\n", "labels": {"reads": [{"table": "bioprocesses", "columns": ["worker_id", "studentid"]}, {"table": "useracct", "columns": ["salinity", "funding_source", "asset_id", "student_name"]}], "writes": [{"table": "exhibitionsartworks", "columns": ["salinity", "funding_source", "asset_id", "student_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO size SELECT county_name, production_value, incident_type_description, customer_id FROM healthcare_access_v2 WHERE county_name > 385\")\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": ["county_name", "production_value", "incident_type_description", "customer_id"]}], "writes": [{"table": "size", "columns": ["county_name", "production_value", "incident_type_description", "customer_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO episodes SELECT service_type_code, community FROM oceania_countries WHERE service_type_code > 123\"\n", "labels": {"reads": [{"table": "oceania_countries", "columns": ["service_type_code", "community"]}], "writes": [{"table": "episodes", "columns": ["service_type_code", "community"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO decentralized_apps SELECT facility_name, fate, workshop_group_id FROM chip_model WHERE facility_name > 354\")\n", "labels": {"reads": [{"table": "chip_model", "columns": ["facility_name", "fate", "workshop_group_id"]}], "writes": [{"table": "decentralized_apps", "columns": ["facility_name", "fate", "workshop_group_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO member_of_club SELECT batting_average, claim_status_description, attraction_type_code, occupancy_rate FROM tvshows WHERE batting_average > 252\"], check=True)\n", "labels": {"reads": [{"table": "tvshows", "columns": ["batting_average", "claim_status_description", "attraction_type_code", "occupancy_rate"]}], "writes": [{"table": "member_of_club", "columns": ["batting_average", "claim_status_description", "attraction_type_code", "occupancy_rate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM instructor\", conn)\ndf.to_sql(\"topublictransportation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "instructor", "columns": null}], "writes": [{"table": "topublictransportation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO wells (event_location, ram_mib) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "wells", "columns": ["event_location", "ram_mib"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO company_info SELECT rest_id, line_name FROM carbon_offset_south_america WHERE rest_id > 466\")\n", "labels": {"reads": [{"table": "carbon_offset_south_america", "columns": ["rest_id", "line_name"]}], "writes": [{"table": "company_info", "columns": ["rest_id", "line_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"infrastructureprojects\")\nwrite_to_sink(df, \"instructors\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": null}], "writes": [{"table": "instructors", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO soccer_teams SELECT platform_name, fastestlapspeed FROM organic_cosmetics WHERE platform_name > 385\")\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": ["platform_name", "fastestlapspeed"]}], "writes": [{"table": "soccer_teams", "columns": ["platform_name", "fastestlapspeed"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_campaigns_daily\").toPandas()\ndf[[\"milliseconds\", \"nutrient_level\"]].to_sql(\"hotel_business_partnerships\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": null}], "writes": [{"table": "hotel_business_partnerships", "columns": ["milliseconds", "nutrient_level"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT initiativeid, vessel_id FROM settlements\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"organization_contact_individuals\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "settlements", "columns": ["initiativeid", "vessel_id"]}], "writes": [{"table": "organization_contact_individuals", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO oceanography SELECT recorded_by_staff_id, era, half FROM algorithmic_fairness_incidents_monthly WHERE recorded_by_staff_id > 281\")\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents_monthly", "columns": ["recorded_by_staff_id", "era", "half"]}], "writes": [{"table": "oceanography", "columns": ["recorded_by_staff_id", "era", "half"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cybersecurity.strategies SELECT carrierid, num_shariah_compliant_investments, dec FROM initiatives WHERE carrierid > 340\"\n", "labels": {"reads": [{"table": "initiatives", "columns": ["carrierid", "num_shariah_compliant_investments", "dec"]}], "writes": [{"table": "cybersecurity.strategies", "columns": ["carrierid", "num_shariah_compliant_investments", "dec"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"communitycourtcases\");\ndf.write().mode(\"overwrite\").saveAsTable(\"initiative_types\");\n", "labels": {"reads": [{"table": "communitycourtcases", "columns": null}], "writes": [{"table": "initiative_types", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"trust\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"demographics\")\n", "labels": {"reads": [{"table": "trust", "columns": null}], "writes": [{"table": "demographics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 118;\nSQL\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": ["trainingyear", "cvid"]}, {"table": "safetytestingcounts", "columns": ["channel_code", "artifact_id"]}], "writes": [{"table": "convictions", "columns": ["channel_code", "artifact_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"restorative_justice_sentences\");\ndf.write().mode(\"overwrite\").saveAsTable(\"stg.stg_products_delta\");\n", "labels": {"reads": [{"table": "restorative_justice_sentences", "columns": null}], "writes": [{"table": "stg.stg_products_delta", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mines\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"prescribes\")\n", "labels": {"reads": [{"table": "mines", "columns": null}], "writes": [{"table": "prescribes", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 182;\nEOF\n", "labels": {"reads": [{"table": "policy_feedback", "columns": ["eliminated_by", "water_depth", "donor_program", "defense_contractor_id"]}], "writes": [{"table": "creative_ai_applications", "columns": ["eliminated_by", "water_depth", "donor_program", "defense_contractor_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table mart_campaigns_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart_campaigns_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"organizations\")\nupsert_to_target(df, \"savings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "organizations", "columns": null}], "writes": [{"table": "savings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vehicle_maintenance SELECT height, bank_name FROM train_station WHERE height > 217\"\n", "labels": {"reads": [{"table": "train_station", "columns": ["height", "bank_name"]}], "writes": [{"table": "vehicle_maintenance", "columns": ["height", "bank_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 242;\nSQL\n", "labels": {"reads": [{"table": "college", "columns": ["marketing_region_name", "budget_allocated"]}, {"table": "materials_usage", "columns": ["member_name", "airport_id", "patientid"]}], "writes": [{"table": "inspection", "columns": ["member_name", "airport_id", "patientid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"railway\").toPandas()\ndf[[\"treatment_date\", \"star_rating_description\"]].to_sql(\"eventattendance\", engine, index=False)\n", "labels": {"reads": [{"table": "railway", "columns": null}], "writes": [{"table": "eventattendance", "columns": ["treatment_date", "star_rating_description"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"artistsdemographics\")\nsrc.write.insertInto(\"carbon_offset_south_america\", overwrite=True)\n", "labels": {"reads": [{"table": "artistsdemographics", "columns": null}], "writes": [{"table": "carbon_offset_south_america", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model film_category depends on bike_share\ndbt run -s film_category --vars 'source: bike_share'\n", "labels": {"reads": [{"table": "bike_share", "columns": null}], "writes": [{"table": "film_category", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO divisions SELECT a.contact_number, b.delegate FROM concert_sales a JOIN skills b ON a.labor_hour_id = b.labor_hour_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "concert_sales", "columns": null}, {"table": "skills", "columns": null}], "writes": [{"table": "divisions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table digital_trends --columns personnel,oil_production --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "digital_trends", "columns": ["personnel", "oil_production"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO climate_finance_asia SELECT complaint_type_code, mental_health_rating, fastestlapspeed, crop FROM field5 WHERE complaint_type_code > 487\"\n", "labels": {"reads": [{"table": "field5", "columns": ["complaint_type_code", "mental_health_rating", "fastestlapspeed", "crop"]}], "writes": [{"table": "climate_finance_asia", "columns": ["complaint_type_code", "mental_health_rating", "fastestlapspeed", "crop"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.coupon_use_daily\", conn)\ndf.to_sql(\"stg.refunds_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": null}], "writes": [{"table": "stg.refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table bi.member_point_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO genetic_research SELECT supplier_country, pet_age, fault_status FROM communitypolicingcenters WHERE supplier_country > 304\"\n", "labels": {"reads": [{"table": "communitypolicingcenters", "columns": ["supplier_country", "pet_age", "fault_status"]}], "writes": [{"table": "genetic_research", "columns": ["supplier_country", "pet_age", "fault_status"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"crops_table\").where(\"dt = current_date()\").writeTo(\"stg.member_point_df\").append()\n", "labels": {"reads": [{"table": "crops_table", "columns": null}], "writes": [{"table": "stg.member_point_df", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO sports (fault_log_entry_id, innovation) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "sports", "columns": ["fault_log_entry_id", "innovation"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sites (document_code, typeid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sites", "columns": ["document_code", "typeid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO contractorsales SELECT a.start_station_name, b.pixels FROM whale_sightings a JOIN chemical_processes b ON a.genrename = b.genrename\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "whale_sightings", "columns": null}, {"table": "chemical_processes", "columns": null}], "writes": [{"table": "contractorsales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"concentrateprices\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "concentrateprices", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"freightforwarding\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "freightforwarding", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"projecttimelinebybudget\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"disabilityadvocacy\")\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": null}], "writes": [{"table": "disabilityadvocacy", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"follows\").toPandas()\ndf[[\"co2_amount\", \"accommodationtype\"]].to_sql(\"automation_tech\", engine, index=False)\n", "labels": {"reads": [{"table": "follows", "columns": null}], "writes": [{"table": "automation_tech", "columns": ["co2_amount", "accommodationtype"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT gamegenre, casetype FROM ocean_floor_mapping\", engine)\nimport logging\ndf.to_sql(\"infra_diversification\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ocean_floor_mapping", "columns": ["gamegenre", "casetype"]}], "writes": [{"table": "infra_diversification", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 137;\nSQL\n", "labels": {"reads": [{"table": "military_bases", "columns": ["course_completion", "support_id"]}, {"table": "loan", "columns": ["instrument", "waste_amount"]}], "writes": [{"table": "mart_payments_df", "columns": ["instrument", "waste_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.stg_clicks_delta SELECT vesselname, collection_id, dishname, day_of_week FROM bi_refunds_daily WHERE vesselname > 79\"\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["vesselname", "collection_id", "dishname", "day_of_week"]}], "writes": [{"table": "stg.stg_clicks_delta", "columns": ["vesselname", "collection_id", "dishname", "day_of_week"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO follows SELECT ethnicity, trade FROM vehicle_registrations WHERE ethnicity > 197\"\n", "labels": {"reads": [{"table": "vehicle_registrations", "columns": ["ethnicity", "trade"]}], "writes": [{"table": "follows", "columns": ["ethnicity", "trade"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO maintenance_contracts SELECT duration_ms, teamid, dispensary_name, drought_id FROM military_sales WHERE duration_ms > 377\")\n", "labels": {"reads": [{"table": "military_sales", "columns": ["duration_ms", "teamid", "dispensary_name", "drought_id"]}], "writes": [{"table": "maintenance_contracts", "columns": ["duration_ms", "teamid", "dispensary_name", "drought_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO farms (jobtitle, high_temperature) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "farms", "columns": ["jobtitle", "high_temperature"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO workshop SELECT min_depth, genre_is, is_organic FROM multimodal_trips WHERE min_depth > 336\"\n", "labels": {"reads": [{"table": "multimodal_trips", "columns": ["min_depth", "genre_is", "is_organic"]}], "writes": [{"table": "workshop", "columns": ["min_depth", "genre_is", "is_organic"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO manager_award SELECT pet_age, amount_payment, sd_id, area FROM gamedesign WHERE pet_age > 143\")\n", "labels": {"reads": [{"table": "gamedesign", "columns": ["pet_age", "amount_payment", "sd_id", "area"]}], "writes": [{"table": "manager_award", "columns": ["pet_age", "amount_payment", "sd_id", "area"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"contract_states\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "contract_states", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.risk_score_di SELECT port_code, dockingdate, directed_by FROM size WHERE port_code > 41\"\n", "labels": {"reads": [{"table": "size", "columns": ["port_code", "dockingdate", "directed_by"]}], "writes": [{"table": "stg.risk_score_di", "columns": ["port_code", "dockingdate", "directed_by"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO therapy_attendance (ota_id, contract_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "therapy_attendance", "columns": ["ota_id", "contract_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM carbon_pricing\"\n", "labels": {"reads": [{"table": "carbon_pricing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 397;\nEOF\n", "labels": {"reads": [{"table": "league_x", "columns": ["inclusive_housing_policy", "credit_score", "theftdate"]}], "writes": [{"table": "recyclers", "columns": ["inclusive_housing_policy", "credit_score", "theftdate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"caribbean_tourists\")\nsrc.write.insertInto(\"arctic_sightings\", overwrite=True)\n", "labels": {"reads": [{"table": "caribbean_tourists", "columns": null}], "writes": [{"table": "arctic_sightings", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 317;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": ["sid", "productiondate"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["sid", "productiondate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fair_trade_suppliers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"veteran_employment\")\n", "labels": {"reads": [{"table": "fair_trade_suppliers", "columns": null}], "writes": [{"table": "veteran_employment", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO government_funding SELECT artifacttype, initiative_id, strain_name FROM menuitems WHERE artifacttype > 351\")\n", "labels": {"reads": [{"table": "menuitems", "columns": ["artifacttype", "initiative_id", "strain_name"]}], "writes": [{"table": "government_funding", "columns": ["artifacttype", "initiative_id", "strain_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO ads_exposure_hourly SELECT hotel_id, document_structure_code, away_team_points, acc_regular_season FROM incarcerated WHERE hotel_id > 436\")\n", "labels": {"reads": [{"table": "incarcerated", "columns": ["hotel_id", "document_structure_code", "away_team_points", "acc_regular_season"]}], "writes": [{"table": "ads_exposure_hourly", "columns": ["hotel_id", "document_structure_code", "away_team_points", "acc_regular_season"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM epl_teams\", conn)\ndf.to_sql(\"attendee_demographics\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "epl_teams", "columns": null}], "writes": [{"table": "attendee_demographics", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bridgerainfall SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wins SELECT * FROM legacy\ncur.execute(\"SELECT license_number, warehousename FROM geneva_motor_show LIMIT 388\")\n", "labels": {"reads": [{"table": "geneva_motor_show", "columns": ["license_number", "warehousename"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO production_costs SELECT grant_end_date, temporary_acting, game_id FROM courtcases WHERE grant_end_date > 32\")\n", "labels": {"reads": [{"table": "courtcases", "columns": ["grant_end_date", "temporary_acting", "game_id"]}], "writes": [{"table": "production_costs", "columns": ["grant_end_date", "temporary_acting", "game_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"regulatory_frameworks\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"sports\")\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": null}], "writes": [{"table": "sports", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dws.payments_delta SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO party_events SELECT round, founder, crane_id FROM public_transportation_routes WHERE round > 345\"\n", "labels": {"reads": [{"table": "public_transportation_routes", "columns": ["round", "founder", "crane_id"]}], "writes": [{"table": "party_events", "columns": ["round", "founder", "crane_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model mailshot_campaigns depends on urban_initiatives\ndbt build --select mailshot_campaigns --vars '{\"src\":\"urban_initiatives\"}'\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": null}], "writes": [{"table": "mailshot_campaigns", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT date_of_enrolment, awards FROM user_video_view LIMIT 122\")\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ocean_species SELECT year, committee, rig_name, other_characteristic_details FROM pilot WHERE year > 81\")\n", "labels": {"reads": [{"table": "user_video_view", "columns": ["date_of_enrolment", "awards"]}, {"table": "pilot", "columns": ["year", "committee", "rig_name", "other_characteristic_details"]}], "writes": [{"table": "ocean_species", "columns": ["year", "committee", "rig_name", "other_characteristic_details"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO city SELECT * FROM legacy\nspark.sql(\"INSERT INTO ads_coupon_use_full SELECT catalog_name, warehouseid, event_type FROM soccer_teams WHERE catalog_name > 208\")\n", "labels": {"reads": [{"table": "soccer_teams", "columns": ["catalog_name", "warehouseid", "event_type"]}], "writes": [{"table": "ads_coupon_use_full", "columns": ["catalog_name", "warehouseid", "event_type"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stores_2 SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mart.mart_member_point_df\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "mart.mart_member_point_df", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO manager (offset_id, treatment_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "manager", "columns": ["offset_id", "treatment_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ai_systems (farm_name, postal_code) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ai_systems", "columns": ["farm_name", "postal_code"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO militarybases SELECT a.degrees, b.dept_address FROM temperatureanomalies a JOIN articles_es b ON a.sellingprice = b.sellingprice\"\n", "labels": {"reads": [{"table": "temperatureanomalies", "columns": null}, {"table": "articles_es", "columns": null}], "writes": [{"table": "militarybases", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO humanitarian_assistance SELECT has_access, sale_quantity FROM wind_projects WHERE has_access > 90\")\n", "labels": {"reads": [{"table": "wind_projects", "columns": ["has_access", "sale_quantity"]}], "writes": [{"table": "humanitarian_assistance", "columns": ["has_access", "sale_quantity"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO smartcontracts SELECT * FROM legacy\nspark.sql(\"INSERT INTO agri_innovations SELECT equipment_id, productname, operation_id FROM marathons WHERE equipment_id > 10\")\n", "labels": {"reads": [{"table": "marathons", "columns": ["equipment_id", "productname", "operation_id"]}], "writes": [{"table": "agri_innovations", "columns": ["equipment_id", "productname", "operation_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.billing_state > 342).all()\n# src table: exhibition_record\nengine.execute(\"INSERT INTO events SELECT * FROM exhibition_record\")\n", "labels": {"reads": [{"table": "exhibition_record", "columns": null}], "writes": [{"table": "events", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO functional_areas SELECT warehousename, bioprocess_id, maxoccupancy, customer_first_name FROM safety_violations WHERE warehousename > 193\");\n", "labels": {"reads": [{"table": "safety_violations", "columns": ["warehousename", "bioprocess_id", "maxoccupancy", "customer_first_name"]}], "writes": [{"table": "functional_areas", "columns": ["warehousename", "bioprocess_id", "maxoccupancy", "customer_first_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO online_platform SELECT trainingyear, class_section, tour_name FROM daily_revenue WHERE trainingyear > 35\"\n", "labels": {"reads": [{"table": "daily_revenue", "columns": ["trainingyear", "class_section", "tour_name"]}], "writes": [{"table": "online_platform", "columns": ["trainingyear", "class_section", "tour_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO onlineengagement SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table documents --target-dir /tmp/land\n", "labels": {"reads": [{"table": "documents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO all_documents SELECT date_joined_staff, cruelty_free, destruction_authorised_by_employee_id, total_passengers FROM ads.refunds_delta WHERE date_joined_staff > 400\");\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": ["date_joined_staff", "cruelty_free", "destruction_authorised_by_employee_id", "total_passengers"]}], "writes": [{"table": "all_documents", "columns": ["date_joined_staff", "cruelty_free", "destruction_authorised_by_employee_id", "total_passengers"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"port_office\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"home_game\")\n", "labels": {"reads": [{"table": "port_office", "columns": null}], "writes": [{"table": "home_game", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"laborstatistics\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "laborstatistics", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dws_coupon_use_df SELECT trainingtype, patient_age, co2_offset_amount, organization_name FROM episodes WHERE trainingtype > 222\"\n", "labels": {"reads": [{"table": "episodes", "columns": ["trainingtype", "patient_age", "co2_offset_amount", "organization_name"]}], "writes": [{"table": "dws_coupon_use_df", "columns": ["trainingtype", "patient_age", "co2_offset_amount", "organization_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"performingartsprograms\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"audience_demographics\")\n", "labels": {"reads": [{"table": "performingartsprograms", "columns": null}], "writes": [{"table": "audience_demographics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO bi.bi_orders_daily SELECT founders, amount_paid FROM reporters WHERE founders > 138\")\n", "labels": {"reads": [{"table": "reporters", "columns": ["founders", "amount_paid"]}], "writes": [{"table": "bi.bi_orders_daily", "columns": ["founders", "amount_paid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 465;\nEOF\n", "labels": {"reads": [{"table": "acceptance", "columns": ["organisation_type", "laborproductivity", "winning_aircraft"]}], "writes": [{"table": "arrivals", "columns": ["organisation_type", "laborproductivity", "winning_aircraft"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.advocate_name > 187).all()\n# src table: farm_competition\nengine.execute(\"INSERT INTO staff_members SELECT * FROM farm_competition\")\n", "labels": {"reads": [{"table": "farm_competition", "columns": null}], "writes": [{"table": "staff_members", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO project_timeline (nutrient_level, workout_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "project_timeline", "columns": ["nutrient_level", "workout_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"education_union\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mentalhealthproviders\")\n", "labels": {"reads": [{"table": "education_union", "columns": null}], "writes": [{"table": "mentalhealthproviders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO visitor_statistics SELECT * FROM legacy\nspark.sql(\"INSERT INTO project_issues SELECT runtime, gendername FROM forest_species WHERE runtime > 90\")\n", "labels": {"reads": [{"table": "forest_species", "columns": ["runtime", "gendername"]}], "writes": [{"table": "project_issues", "columns": ["runtime", "gendername"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM co_ownership_program\"\n", "labels": {"reads": [{"table": "co_ownership_program", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO mental_health_parity SELECT * FROM legacy\nspark.sql(\"INSERT INTO cargo_tracking SELECT crop_name, testtype, evaluationid, blockcode FROM country_renewable_energy WHERE crop_name > 376\")\n", "labels": {"reads": [{"table": "country_renewable_energy", "columns": ["crop_name", "testtype", "evaluationid", "blockcode"]}], "writes": [{"table": "cargo_tracking", "columns": ["crop_name", "testtype", "evaluationid", "blockcode"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 32;\nSQL\n", "labels": {"reads": [{"table": "police_stations", "columns": ["workshop_name", "metric"]}, {"table": "sustainable_menu_items", "columns": ["community_type", "society", "signupdate"]}], "writes": [{"table": "energy_storage", "columns": ["community_type", "society", "signupdate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ais SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rd_expenditure\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "rd_expenditure", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO flu_shots SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 86;\nEOF\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": ["year_built", "siteid", "energy_generated", "party_id"]}], "writes": [{"table": "suppliersfairlabor", "columns": ["year_built", "siteid", "energy_generated", "party_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_inventory_delta\"\n", "labels": {"reads": [{"table": "bi.bi_inventory_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model events_df depends on pediatricians\ndbt build -s events_df --vars 'source: pediatricians'\n", "labels": {"reads": [{"table": "pediatricians", "columns": null}], "writes": [{"table": "events_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO spacecrafts SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO manufacturermaterials SELECT total_shipped, well_name FROM multimodalhubs WHERE total_shipped > 54\")\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": ["total_shipped", "well_name"]}], "writes": [{"table": "manufacturermaterials", "columns": ["total_shipped", "well_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO machines SELECT employer_organisation_id, bedroom_count, energy_efficiency_rating FROM public_transportation_routes WHERE employer_organisation_id > 405\"\n", "labels": {"reads": [{"table": "public_transportation_routes", "columns": ["employer_organisation_id", "bedroom_count", "energy_efficiency_rating"]}], "writes": [{"table": "machines", "columns": ["employer_organisation_id", "bedroom_count", "energy_efficiency_rating"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO renewabletypes SELECT staff_address_id, training_name, minutes, quantitysold FROM dates WHERE staff_address_id > 329\"\n", "labels": {"reads": [{"table": "dates", "columns": ["staff_address_id", "training_name", "minutes", "quantitysold"]}], "writes": [{"table": "renewabletypes", "columns": ["staff_address_id", "training_name", "minutes", "quantitysold"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO teaches SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"cities\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "cities", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"party_services\").toPandas()\ndf[[\"is_eco_friendly\", \"event_type\"]].to_sql(\"business_rates\", engine, index=False)\n", "labels": {"reads": [{"table": "party_services", "columns": null}], "writes": [{"table": "business_rates", "columns": ["is_eco_friendly", "event_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"textileworkers\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "textileworkers", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM wastedata\"\n", "labels": {"reads": [{"table": "wastedata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO ods.coupon_use SELECT status_of_thing_code, platform_name, recruitername, oil_production_q4_2021 FROM totalenergyproduction WHERE status_of_thing_code > 62\"\n", "labels": {"reads": [{"table": "totalenergyproduction", "columns": ["status_of_thing_code", "platform_name", "recruitername", "oil_production_q4_2021"]}], "writes": [{"table": "ods.coupon_use", "columns": ["status_of_thing_code", "platform_name", "recruitername", "oil_production_q4_2021"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table researchgrants --target-dir /tmp/land\n", "labels": {"reads": [{"table": "researchgrants", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"wellbeing_programs\").toPandas()\ndf[[\"personnel_id\", \"host_city_id\"]].to_sql(\"media_types\", engine, index=False)\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": null}], "writes": [{"table": "media_types", "columns": ["personnel_id", "host_city_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"chemicals\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"mining_companies\")\n", "labels": {"reads": [{"table": "chemicals", "columns": null}], "writes": [{"table": "mining_companies", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO archaeologists (borough, building_full_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "archaeologists", "columns": ["borough", "building_full_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO people SELECT a.driller, b.total_budget_percent_invested FROM stock_levels a JOIN militarycyberops b ON a.category_name = b.category_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stock_levels", "columns": null}, {"table": "militarycyberops", "columns": null}], "writes": [{"table": "people", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO dishes SELECT steps, portname FROM species_forests WHERE steps > 33\");\n", "labels": {"reads": [{"table": "species_forests", "columns": ["steps", "portname"]}], "writes": [{"table": "dishes", "columns": ["steps", "portname"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table eu_data_usage --target-dir /tmp/land\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO product_review (employeeid, amount_outstanding) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "product_review", "columns": ["employeeid", "amount_outstanding"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO grants (avg_yield, support_rep_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "grants", "columns": ["avg_yield", "support_rep_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO green_building_materials SELECT approval_date, browser_id, value FROM vessel_capacity WHERE approval_date > 15\"\n", "labels": {"reads": [{"table": "vessel_capacity", "columns": ["approval_date", "browser_id", "value"]}], "writes": [{"table": "green_building_materials", "columns": ["approval_date", "browser_id", "value"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO employment SELECT a.volunteer_name, b.stu_hrs FROM mart.mart_shipments_hourly a JOIN genre_songs b ON a.product_category_description = b.product_category_description\"\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": null}, {"table": "genre_songs", "columns": null}], "writes": [{"table": "employment", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws.dws_orders_full\")\nsrc.write.insertInto(\"researchpapers\", overwrite=True)\n", "labels": {"reads": [{"table": "dws.dws_orders_full", "columns": null}], "writes": [{"table": "researchpapers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO satellites_in_orbit (section_title, account_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "satellites_in_orbit", "columns": ["section_title", "account_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ocean_species\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "ocean_species", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO communityhealthworkerscanada SELECT * FROM legacy\ncur.execute(\"SELECT creation_year, dockingdate FROM timber_production LIMIT 333\")\n", "labels": {"reads": [{"table": "timber_production", "columns": ["creation_year", "dockingdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO recall_reports SELECT a.county_name, b.num_courses FROM ads.events a JOIN militarybases b ON a.mental_health_status = b.mental_health_status\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.events", "columns": null}, {"table": "militarybases", "columns": null}], "writes": [{"table": "recall_reports", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"drug_sales\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "drug_sales", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO products_booked SELECT a.last_name, b.number_of_matches FROM pollutionincidents a JOIN cotton_source b ON a.therapy_sessions = b.therapy_sessions\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "pollutionincidents", "columns": null}, {"table": "cotton_source", "columns": null}], "writes": [{"table": "products_booked", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"singer_in_concert\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"marine_life_research\")\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": [{"table": "marine_life_research", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ads.ads_refunds_hourly --columns contract_date,songname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_refunds_hourly", "columns": ["contract_date", "songname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO healthcareaccess SELECT claim_amount, q1_2022_views FROM criminal_justice_reform_initiatives WHERE claim_amount > 3\"\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": ["claim_amount", "q1_2022_views"]}], "writes": [{"table": "healthcareaccess", "columns": ["claim_amount", "q1_2022_views"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT devices, fund_name FROM production_costs LIMIT 488\")\nimport logging\nspark.sql(\"INSERT INTO spacecraftmanufacturing SELECT payment_method, killed FROM casesbyyear WHERE payment_method > 72\")\n", "labels": {"reads": [{"table": "production_costs", "columns": ["devices", "fund_name"]}, {"table": "casesbyyear", "columns": ["payment_method", "killed"]}], "writes": [{"table": "spacecraftmanufacturing", "columns": ["payment_method", "killed"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO red_line SELECT src_apid, building_manager, feature_id FROM museums WHERE src_apid > 416\"\n", "labels": {"reads": [{"table": "museums", "columns": ["src_apid", "building_manager", "feature_id"]}], "writes": [{"table": "red_line", "columns": ["src_apid", "building_manager", "feature_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO athlete_wellbeing SELECT premise_id, itemname, assets_billion, working_horses FROM retailers WHERE premise_id > 91\"\n", "labels": {"reads": [{"table": "retailers", "columns": ["premise_id", "itemname", "assets_billion", "working_horses"]}], "writes": [{"table": "athlete_wellbeing", "columns": ["premise_id", "itemname", "assets_billion", "working_horses"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO concerts (supplier_company_id, start_station_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "concerts", "columns": ["supplier_company_id", "start_station_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nsql = \"INSERT INTO stellar_transactions SELECT a.attorney_id, b.cityid FROM maintenance a JOIN categories b ON a.uk_vat_number = b.uk_vat_number\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "maintenance", "columns": null}, {"table": "categories", "columns": null}], "writes": [{"table": "stellar_transactions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO india_ingredient_sourcing SELECT attendance_date, check_in_date, organic_matter, case_id FROM dw.events_hourly WHERE attendance_date > 412\"], check=True)\n", "labels": {"reads": [{"table": "dw.events_hourly", "columns": ["attendance_date", "check_in_date", "organic_matter", "case_id"]}], "writes": [{"table": "india_ingredient_sourcing", "columns": ["attendance_date", "check_in_date", "organic_matter", "case_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO ocean_temperatures SELECT a.service_type_description, b.max_speed FROM stars a JOIN emergency_calls b ON a.allergytype = b.allergytype\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stars", "columns": null}, {"table": "emergency_calls", "columns": null}], "writes": [{"table": "ocean_temperatures", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO food_safety_inspections SELECT * FROM legacy\ncur.execute(\"SELECT vehicle_model, employeeid FROM bi.clicks_df LIMIT 197\")\n", "labels": {"reads": [{"table": "bi.clicks_df", "columns": ["vehicle_model", "employeeid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dorm SELECT access_count, eco_certified, added_date, applicant FROM regions WHERE access_count > 236\"], check=True)\n", "labels": {"reads": [{"table": "regions", "columns": ["access_count", "eco_certified", "added_date", "applicant"]}], "writes": [{"table": "dorm", "columns": ["access_count", "eco_certified", "added_date", "applicant"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM roles\"\n", "labels": {"reads": [{"table": "roles", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO footwear SELECT policy_type_code, certified, amount_outstanding FROM art_workshops WHERE policy_type_code > 447\"\n", "labels": {"reads": [{"table": "art_workshops", "columns": ["policy_type_code", "certified", "amount_outstanding"]}], "writes": [{"table": "footwear", "columns": ["policy_type_code", "certified", "amount_outstanding"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"activities\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "activities", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO company_info SELECT item_type, high_temperature FROM ads.risk_score WHERE item_type > 65\"], check=True)\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": ["item_type", "high_temperature"]}], "writes": [{"table": "company_info", "columns": ["item_type", "high_temperature"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO genetics.projects SELECT event_type, expeditionid, account_type FROM pilot WHERE event_type > 367\")\n", "labels": {"reads": [{"table": "pilot", "columns": ["event_type", "expeditionid", "account_type"]}], "writes": [{"table": "genetics.projects", "columns": ["event_type", "expeditionid", "account_type"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"satisfaction\")\nsrc.write.insertInto(\"menu_vendors\", overwrite=True)\n", "labels": {"reads": [{"table": "satisfaction", "columns": null}], "writes": [{"table": "menu_vendors", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"spacemissions\").toPandas()\ndf[[\"sustainabilityid\", \"underrepresented_community\"]].to_sql(\"hospital_equipment\", engine, index=False)\n", "labels": {"reads": [{"table": "spacemissions", "columns": null}], "writes": [{"table": "hospital_equipment", "columns": ["sustainabilityid", "underrepresented_community"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT main_services, trend FROM climate_projects\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"rainfall_data\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "climate_projects", "columns": ["main_services", "trend"]}], "writes": [{"table": "rainfall_data", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"species_observations\")\nsrc.write.insertInto(\"indigenous_food_systems\", overwrite=True)\n", "labels": {"reads": [{"table": "species_observations", "columns": null}], "writes": [{"table": "indigenous_food_systems", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT customer_first_name, offer_id FROM satellite_missions_large\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"ingredientsvegancrueltyfree\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": ["customer_first_name", "offer_id"]}], "writes": [{"table": "ingredientsvegancrueltyfree", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO historicalcontexts SELECT date_and_date, bridgetype, song_year, museum_details FROM dws_shipments_df WHERE date_and_date > 208\"\n", "labels": {"reads": [{"table": "dws_shipments_df", "columns": ["date_and_date", "bridgetype", "song_year", "museum_details"]}], "writes": [{"table": "historicalcontexts", "columns": ["date_and_date", "bridgetype", "song_year", "museum_details"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"student_access\")\nsrc.write.insertInto(\"fossil_fuel_vehicles\", overwrite=True)\n", "labels": {"reads": [{"table": "student_access", "columns": null}], "writes": [{"table": "fossil_fuel_vehicles", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO catalog_contents_additional_attributes (assists, contributions) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "catalog_contents_additional_attributes", "columns": ["assists", "contributions"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"lenders\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "lenders", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table volunteer_hours --columns report,staff_address_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "volunteer_hours", "columns": ["report", "staff_address_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO brazil_projects SELECT organisation_details, vendor_state, maintenance_contract_id, asset_make FROM navalequipmentmaintenance WHERE organisation_details > 473\")\n", "labels": {"reads": [{"table": "navalequipmentmaintenance", "columns": ["organisation_details", "vendor_state", "maintenance_contract_id", "asset_make"]}], "writes": [{"table": "brazil_projects", "columns": ["organisation_details", "vendor_state", "maintenance_contract_id", "asset_make"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"scores\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "scores", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"deep_sea_species\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"shoes\")\n", "labels": {"reads": [{"table": "deep_sea_species", "columns": null}], "writes": [{"table": "shoes", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO tunnels SELECT architect_id, hospital_id, successful_cb FROM storage_projects WHERE architect_id > 434\")\n", "labels": {"reads": [{"table": "storage_projects", "columns": ["architect_id", "hospital_id", "successful_cb"]}], "writes": [{"table": "tunnels", "columns": ["architect_id", "hospital_id", "successful_cb"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.asset_disposed_date > 264).all()\n# src table: party_forms\nengine.execute(\"INSERT INTO satellite_missions_large SELECT * FROM party_forms\")\n", "labels": {"reads": [{"table": "party_forms", "columns": null}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO higher_ed.publications (min_salary, show_times_per_day) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "higher_ed.publications", "columns": ["min_salary", "show_times_per_day"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"public_works_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ref_locations\")\n", "labels": {"reads": [{"table": "public_works_projects", "columns": null}], "writes": [{"table": "ref_locations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT round_number, releaseyear FROM volunteers\", engine)\nmetrics.append(round(score, 4))\nimport logging\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"co_ownership\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "volunteers", "columns": ["round_number", "releaseyear"]}], "writes": [{"table": "co_ownership", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO whale_sharks SELECT credits, dispensary_name, facilityid FROM family_cases WHERE credits > 354\"\n", "labels": {"reads": [{"table": "family_cases", "columns": ["credits", "dispensary_name", "facilityid"]}], "writes": [{"table": "whale_sharks", "columns": ["credits", "dispensary_name", "facilityid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO contract_negotiations_un SELECT sea, event, sportname FROM euro_champs_track_field WHERE sea > 173\")\n", "labels": {"reads": [{"table": "euro_champs_track_field", "columns": ["sea", "event", "sportname"]}], "writes": [{"table": "contract_negotiations_un", "columns": ["sea", "event", "sportname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO movie_ratings SELECT beds, organization_id, regulation, budgetid FROM bi.bi_member_point WHERE beds > 392\"\n", "labels": {"reads": [{"table": "bi.bi_member_point", "columns": ["beds", "organization_id", "regulation", "budgetid"]}], "writes": [{"table": "movie_ratings", "columns": ["beds", "organization_id", "regulation", "budgetid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"socially_responsible_lending\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"medical_professionals\")\n", "labels": {"reads": [{"table": "socially_responsible_lending", "columns": null}], "writes": [{"table": "medical_professionals", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO professionals SELECT a.school_colors, b.delegate FROM production_costs a JOIN al_jazeera_data b ON a.draft_details = b.draft_details\"\n", "labels": {"reads": [{"table": "production_costs", "columns": null}, {"table": "al_jazeera_data", "columns": null}], "writes": [{"table": "professionals", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"measurement\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "measurement", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT expedition_name, item_price FROM workerbuildings\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"rebounds\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "workerbuildings", "columns": ["expedition_name", "item_price"]}], "writes": [{"table": "rebounds", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"co2emissions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"safety_testing\")\n", "labels": {"reads": [{"table": "co2emissions", "columns": null}], "writes": [{"table": "safety_testing", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mobile_customers_global\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mobile_customers_global", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads_users_hourly SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO recyclingcenters SELECT school_code, store_email_address, number_of_matches FROM customer_address_history WHERE school_code > 368\"], check=True)\n", "labels": {"reads": [{"table": "customer_address_history", "columns": ["school_code", "store_email_address", "number_of_matches"]}], "writes": [{"table": "recyclingcenters", "columns": ["school_code", "store_email_address", "number_of_matches"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO city_budgets SELECT tourists, funding FROM waste_types WHERE tourists > 85\")\n", "labels": {"reads": [{"table": "waste_types", "columns": ["tourists", "funding"]}], "writes": [{"table": "city_budgets", "columns": ["tourists", "funding"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO projects (funding_year, scientific_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "projects", "columns": ["funding_year", "scientific_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"ods.ods_risk_score_delta\")\nwrite_to_output(df, \"stg.stg_inventory_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods.ods_risk_score_delta", "columns": null}], "writes": [{"table": "stg.stg_inventory_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_products_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"contract_transactions\")\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": null}], "writes": [{"table": "contract_transactions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dwd.events_daily\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"city_budgets\")\n", "labels": {"reads": [{"table": "dwd.events_daily", "columns": null}], "writes": [{"table": "city_budgets", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_ad_interactions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"course_attendance\")\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": null}], "writes": [{"table": "course_attendance", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO view_unit_status SELECT incident_region, cid, professional_development_programs FROM weights WHERE incident_region > 344\"\n", "labels": {"reads": [{"table": "weights", "columns": ["incident_region", "cid", "professional_development_programs"]}], "writes": [{"table": "view_unit_status", "columns": ["incident_region", "cid", "professional_development_programs"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads.ads_events_df\"\n", "labels": {"reads": [{"table": "ads.ads_events_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 183;\nSQL\n", "labels": {"reads": [{"table": "impact_investments", "columns": ["taskid", "org_size"]}, {"table": "school", "columns": ["production_id", "gas_production_2020"]}], "writes": [{"table": "member_details", "columns": ["production_id", "gas_production_2020"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dw.dw_member_point_hourly\").where(\"dt = current_date()\").writeTo(\"team_members\").append()\n", "labels": {"reads": [{"table": "dw.dw_member_point_hourly", "columns": null}], "writes": [{"table": "team_members", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO salary SELECT num_of_stock, practiceid, contract_date FROM gameattendance WHERE num_of_stock > 40\"\n", "labels": {"reads": [{"table": "gameattendance", "columns": ["num_of_stock", "practiceid", "contract_date"]}], "writes": [{"table": "salary", "columns": ["num_of_stock", "practiceid", "contract_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO vehiclemodels SELECT date_of_attendance, nutrient_level, date_joined_staff FROM stg.stg_exposure_daily WHERE date_of_attendance > 356\")\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": ["date_of_attendance", "nutrient_level", "date_joined_staff"]}], "writes": [{"table": "vehiclemodels", "columns": ["date_of_attendance", "nutrient_level", "date_joined_staff"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO virtual_visitors SELECT prod_id, subscriber_type, circuitid FROM public.collected_fare WHERE prod_id > 49\"], check=True)\n", "labels": {"reads": [{"table": "public.collected_fare", "columns": ["prod_id", "subscriber_type", "circuitid"]}], "writes": [{"table": "virtual_visitors", "columns": ["prod_id", "subscriber_type", "circuitid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO animal_budget SELECT * FROM legacy\ncur.execute(\"SELECT studentid, allergytype FROM vessel LIMIT 213\")\n", "labels": {"reads": [{"table": "vessel", "columns": ["studentid", "allergytype"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT stuid, submission_id FROM brands LIMIT 389\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "brands", "columns": ["stuid", "submission_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM platformg\"\n", "labels": {"reads": [{"table": "platformg", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.premise_details > 278).all()\n# src table: county_public_safety\nengine.execute(\"INSERT INTO band SELECT * FROM county_public_safety\")\n", "labels": {"reads": [{"table": "county_public_safety", "columns": null}], "writes": [{"table": "band", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO totalenergyproduction SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO mart.mart_refunds_di SELECT founding_year, dorm_name, strat_name, highscore FROM transaction WHERE founding_year > 70\");\n", "labels": {"reads": [{"table": "transaction", "columns": ["founding_year", "dorm_name", "strat_name", "highscore"]}], "writes": [{"table": "mart.mart_refunds_di", "columns": ["founding_year", "dorm_name", "strat_name", "highscore"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wind_farms SELECT * FROM legacy\ncur.execute(\"SELECT provider_name, biomass FROM vessel_performance LIMIT 196\")\n", "labels": {"reads": [{"table": "vessel_performance", "columns": ["provider_name", "biomass"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO community_health_workers SELECT institution, billid, billingid, investmentid FROM smartcityprojects WHERE institution > 326\")\n", "labels": {"reads": [{"table": "smartcityprojects", "columns": ["institution", "billid", "billingid", "investmentid"]}], "writes": [{"table": "community_health_workers", "columns": ["institution", "billid", "billingid", "investmentid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO customer_month SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO checking SELECT * FROM legacy\ncur.execute(\"SELECT patentexpirationdate, platformname FROM criticalincidents LIMIT 263\")\n", "labels": {"reads": [{"table": "criticalincidents", "columns": ["patentexpirationdate", "platformname"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO nba SELECT exploited, inclusivehousing, custid FROM consumer_preference WHERE exploited > 447\"\n", "labels": {"reads": [{"table": "consumer_preference", "columns": ["exploited", "inclusivehousing", "custid"]}], "writes": [{"table": "nba", "columns": ["exploited", "inclusivehousing", "custid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.inventory_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"sustainable_projects\")\n", "labels": {"reads": [{"table": "bi.inventory_daily", "columns": null}], "writes": [{"table": "sustainable_projects", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.events_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 123;\nEOF\n", "labels": {"reads": [{"table": "dwd_events_delta", "columns": ["therapy_id", "license_number", "application"]}], "writes": [{"table": "bi.bi_member_point", "columns": ["therapy_id", "license_number", "application"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.numcases > 460).all()\n# src table: artistsdemographics\nengine.execute(\"INSERT INTO volunteer_signups SELECT * FROM artistsdemographics\")\n", "labels": {"reads": [{"table": "artistsdemographics", "columns": null}], "writes": [{"table": "volunteer_signups", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO ods_vendors_daily SELECT professionalid, experience_id, money_requested FROM ads_sessions_di WHERE professionalid > 304\");\n", "labels": {"reads": [{"table": "ads_sessions_di", "columns": ["professionalid", "experience_id", "money_requested"]}], "writes": [{"table": "ods_vendors_daily", "columns": ["professionalid", "experience_id", "money_requested"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donationsbycause\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"video_content\")\n", "labels": {"reads": [{"table": "donationsbycause", "columns": null}], "writes": [{"table": "video_content", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"budget_allocations\")\nexport_to_target(df, \"singer\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "singer", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.stu_phone > 100).all()\n# src table: stg.risk_score_di\nengine.execute(\"INSERT INTO emergency_responses SELECT * FROM stg.risk_score_di\")\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": null}], "writes": [{"table": "emergency_responses", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO heart_rate_data SELECT a.tech, b.account_name FROM diseases a JOIN military_spending b ON a.citizens = b.citizens\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "diseases", "columns": null}, {"table": "military_spending", "columns": null}], "writes": [{"table": "heart_rate_data", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO vehicle_sales SELECT athlete_id, total_amount_purchased, dysprosium_prod FROM artcontributors WHERE athlete_id > 491\"\n", "labels": {"reads": [{"table": "artcontributors", "columns": ["athlete_id", "total_amount_purchased", "dysprosium_prod"]}], "writes": [{"table": "vehicle_sales", "columns": ["athlete_id", "total_amount_purchased", "dysprosium_prod"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_initiatives\").toPandas()\ndf[[\"loadingend\", \"vehicle_id\"]].to_sql(\"hydro_power\", engine, index=False)\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": null}], "writes": [{"table": "hydro_power", "columns": ["loadingend", "vehicle_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO militaryoperations SELECT target_name, membership_amount, dispensary_id, unionid FROM dws.dws_orders WHERE target_name > 328\")\n", "labels": {"reads": [{"table": "dws.dws_orders", "columns": ["target_name", "membership_amount", "dispensary_id", "unionid"]}], "writes": [{"table": "militaryoperations", "columns": ["target_name", "membership_amount", "dispensary_id", "unionid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM satellites\"\n", "labels": {"reads": [{"table": "satellites", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"news_reporting\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"daily_revenue\")\n", "labels": {"reads": [{"table": "news_reporting", "columns": null}], "writes": [{"table": "daily_revenue", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 23;\nEOF\n", "labels": {"reads": [{"table": "workforce_training", "columns": ["opponent_id", "minesite", "fault_description"]}], "writes": [{"table": "dws.dws_member_point_di", "columns": ["opponent_id", "minesite", "fault_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"donor\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "donor", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO experience SELECT vehicleid, acc_bal, musical_id, employeeid FROM dwd.dwd_campaigns WHERE vehicleid > 329\"\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns", "columns": ["vehicleid", "acc_bal", "musical_id", "employeeid"]}], "writes": [{"table": "experience", "columns": ["vehicleid", "acc_bal", "musical_id", "employeeid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"climateresearch\")\nsrc.write.insertInto(\"bank_info\", overwrite=True)\n", "labels": {"reads": [{"table": "climateresearch", "columns": null}], "writes": [{"table": "bank_info", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.fault_status > 311).all()\n# src table: southchinasea.wells\nengine.execute(\"INSERT INTO e_scooter_trips SELECT * FROM southchinasea.wells\")\n", "labels": {"reads": [{"table": "southchinasea.wells", "columns": null}], "writes": [{"table": "e_scooter_trips", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.coupon_use\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"basketball_teams\")\n", "labels": {"reads": [{"table": "ods.coupon_use", "columns": null}], "writes": [{"table": "basketball_teams", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_source(ctx, \"midwest_materials\")\nexport_to_target(df, \"roles\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "midwest_materials", "columns": null}], "writes": [{"table": "roles", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO climate_communication SELECT fundingid, portid, document_status_description, farmname FROM procedures WHERE fundingid > 182\")\n", "labels": {"reads": [{"table": "procedures", "columns": ["fundingid", "portid", "document_status_description", "farmname"]}], "writes": [{"table": "climate_communication", "columns": ["fundingid", "portid", "document_status_description", "farmname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"project_issues\");\ndf.write().mode(\"overwrite\").saveAsTable(\"green_building_projects\");\n", "labels": {"reads": [{"table": "project_issues", "columns": null}], "writes": [{"table": "green_building_projects", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ads_vendors_hourly SELECT nationality, department_id, artifactid, business_size FROM vehicle_registrations WHERE nationality > 245\")\n", "labels": {"reads": [{"table": "vehicle_registrations", "columns": ["nationality", "department_id", "artifactid", "business_size"]}], "writes": [{"table": "ads_vendors_hourly", "columns": ["nationality", "department_id", "artifactid", "business_size"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"creative_ai\");\ndf.write().mode(\"overwrite\").saveAsTable(\"shared_ebikes\");\n", "labels": {"reads": [{"table": "creative_ai", "columns": null}], "writes": [{"table": "shared_ebikes", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods.ods_coupon_use_di SELECT a.strat_name, b.feature_details FROM mart.mart_device_log_delta a JOIN water_treatment_facilities b ON a.home_team_score = b.home_team_score\"\n", "labels": {"reads": [{"table": "mart.mart_device_log_delta", "columns": null}, {"table": "water_treatment_facilities", "columns": null}], "writes": [{"table": "ods.ods_coupon_use_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM agri_innov\", conn)\ndf.to_sql(\"public.forest_stats\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "agri_innov", "columns": null}], "writes": [{"table": "public.forest_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model bi_inventory_full depends on education\ndbt run -s bi_inventory_full --vars '{\"source_table\":\"education\"}'\n", "labels": {"reads": [{"table": "education", "columns": null}], "writes": [{"table": "bi_inventory_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table ads.ads_products_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.team_id_loser > 433).all()\n# src table: voyages\nengine.execute(\"INSERT INTO ods.clicks_delta SELECT * FROM voyages\")\n", "labels": {"reads": [{"table": "voyages", "columns": null}], "writes": [{"table": "ods.clicks_delta", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO workforce_training SELECT metric_id, client, hourid FROM animal_population_status WHERE metric_id > 89\"\n", "labels": {"reads": [{"table": "animal_population_status", "columns": ["metric_id", "client", "hourid"]}], "writes": [{"table": "workforce_training", "columns": ["metric_id", "client", "hourid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM user\"\n", "labels": {"reads": [{"table": "user", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO environmental_impact_stats SELECT image_name, flight_number, donation_date FROM bioprocess.engineering_projects WHERE image_name > 50\");\n", "labels": {"reads": [{"table": "bioprocess.engineering_projects", "columns": ["image_name", "flight_number", "donation_date"]}], "writes": [{"table": "environmental_impact_stats", "columns": ["image_name", "flight_number", "donation_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO labor_hours SELECT * FROM legacy\ncur.execute(\"SELECT uses_vr, teamid FROM english_premier_league LIMIT 498\")\n", "labels": {"reads": [{"table": "english_premier_league", "columns": ["uses_vr", "teamid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO territory.human_rights_data SELECT is_recycled, description FROM ods.inventory_df WHERE is_recycled > 374\"], check=True)\n", "labels": {"reads": [{"table": "ods.inventory_df", "columns": ["is_recycled", "description"]}], "writes": [{"table": "territory.human_rights_data", "columns": ["is_recycled", "description"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO dws.dws_coupon_use_full SELECT revenue, stu_lname FROM solar_energy WHERE revenue > 395\");\n", "labels": {"reads": [{"table": "solar_energy", "columns": ["revenue", "stu_lname"]}], "writes": [{"table": "dws.dws_coupon_use_full", "columns": ["revenue", "stu_lname"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"cultural_competency_training\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"humanitarian_operations\")\n", "labels": {"reads": [{"table": "cultural_competency_training", "columns": null}], "writes": [{"table": "humanitarian_operations", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"virtual_tour_revenue\")\nsrc.write.insertInto(\"support\", overwrite=True)\n", "labels": {"reads": [{"table": "virtual_tour_revenue", "columns": null}], "writes": [{"table": "support", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table road_construction --columns image_data,donor_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "road_construction", "columns": ["image_data", "donor_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods.ods_campaigns_df SELECT * FROM legacy\ncur.execute(\"SELECT available_yn, retail_price FROM vrusers LIMIT 210\")\n", "labels": {"reads": [{"table": "vrusers", "columns": ["available_yn", "retail_price"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO disinformation_detection SELECT tonnage, inventoryid FROM degrees WHERE tonnage > 435\"\n", "labels": {"reads": [{"table": "degrees", "columns": ["tonnage", "inventoryid"]}], "writes": [{"table": "disinformation_detection", "columns": ["tonnage", "inventoryid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"conservation\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "conservation", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dailystreams SELECT a.hometown, b.show_id FROM course_authors_and_tutors a JOIN stg.stg_users_full b ON a.routeid = b.routeid\"\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": null}, {"table": "stg.stg_users_full", "columns": null}], "writes": [{"table": "dailystreams", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO investments SELECT policyholder_id, sustainability_rating FROM station_crime_rates WHERE policyholder_id > 148\"\n", "labels": {"reads": [{"table": "station_crime_rates", "columns": ["policyholder_id", "sustainability_rating"]}], "writes": [{"table": "investments", "columns": ["policyholder_id", "sustainability_rating"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table energy_efficiency_projects --columns sensor_type,countid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "energy_efficiency_projects", "columns": ["sensor_type", "countid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT maintenance_type, squadron FROM soil_moisture LIMIT 457\")\nrows = cur.fetchall()\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "soil_moisture", "columns": ["maintenance_type", "squadron"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ads.ads_vendors_hourly\", conn)\ndf.to_sql(\"fishcaught\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_vendors_hourly", "columns": null}], "writes": [{"table": "fishcaught", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws_shipments_df SELECT * FROM legacy\ncur.execute(\"SELECT retailer_id, length_feet FROM customer_address_history LIMIT 218\")\n", "labels": {"reads": [{"table": "customer_address_history", "columns": ["retailer_id", "length_feet"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ethical_ai (org_name, union_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ethical_ai", "columns": ["org_name", "union_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT funding_source_type, meal_id FROM districts_india\", engine)\nlogger = logging.getLogger(__name__)\nimport logging\ndf.to_sql(\"timed_status_of_things\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "districts_india", "columns": ["funding_source_type", "meal_id"]}], "writes": [{"table": "timed_status_of_things", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO portfolios (character, is_commercial) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "portfolios", "columns": ["character", "is_commercial"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 415;\nSQL\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": ["max_depth", "milliseconds"]}, {"table": "dw_member_point_full", "columns": ["request_id", "type", "opname"]}], "writes": [{"table": "provider_training", "columns": ["request_id", "type", "opname"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 217;\nEOF\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": ["checkout", "last_maintenance_date", "date_in_locaton_to", "practices"]}], "writes": [{"table": "mart.vendors_full", "columns": ["checkout", "last_maintenance_date", "date_in_locaton_to", "practices"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fraud_detections\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "fraud_detections", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table weapons --columns shariah_compliant_investment_amount,strainid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "weapons", "columns": ["shariah_compliant_investment_amount", "strainid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sales_quarterly SELECT organizationid, characteristic_id, volunteerage, impact_score FROM dwd.dwd_exposure_full WHERE organizationid > 301\"\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_full", "columns": ["organizationid", "characteristic_id", "volunteerage", "impact_score"]}], "writes": [{"table": "sales_quarterly", "columns": ["organizationid", "characteristic_id", "volunteerage", "impact_score"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO makeup_sales SELECT album, savings, employee_id, address_details FROM ethics_violations WHERE album > 236\");\n", "labels": {"reads": [{"table": "ethics_violations", "columns": ["album", "savings", "employee_id", "address_details"]}], "writes": [{"table": "makeup_sales", "columns": ["album", "savings", "employee_id", "address_details"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw_users_full\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw_users_full", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO monthly_temp SELECT a.union_id, b.attendees FROM basketball_match a JOIN intelligenceoperations b ON a.playergameid = b.playergameid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "basketball_match", "columns": null}, {"table": "intelligenceoperations", "columns": null}], "writes": [{"table": "monthly_temp", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"problem_log\").where(\"dt = current_date()\").writeTo(\"benefits_overpayments\").append()\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": [{"table": "benefits_overpayments", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 113;\nSQL\n", "labels": {"reads": [{"table": "dws.risk_score_daily", "columns": ["field", "transaction_date"]}, {"table": "public_transport.passenger_count", "columns": ["customer_address", "dockingdate", "received_date", "investment_id"]}], "writes": [{"table": "dwd.dwd_campaigns_df", "columns": ["customer_address", "dockingdate", "received_date", "investment_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO apac_hotel_views SELECT 1\"\nlogger.info(msg)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO beverages SELECT staff_name, post_category FROM birds WHERE staff_name > 439\")\n", "labels": {"reads": [{"table": "birds", "columns": ["staff_name", "post_category"]}], "writes": [{"table": "beverages", "columns": ["staff_name", "post_category"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO droughthistory SELECT a.class_president_vote, b.crop FROM pilot a JOIN haircare_sales b ON a.subscription_start_date = b.subscription_start_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "pilot", "columns": null}, {"table": "haircare_sales", "columns": null}], "writes": [{"table": "droughthistory", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"research_staff\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart.shipments_delta\")\n", "labels": {"reads": [{"table": "research_staff", "columns": null}], "writes": [{"table": "mart.shipments_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO ads.inventory_di SELECT phone, vehicle, status FROM mental_health_parity_violations WHERE phone > 192\");\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": ["phone", "vehicle", "status"]}], "writes": [{"table": "ads.inventory_di", "columns": ["phone", "vehicle", "status"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT reports_to, participant_details FROM mart.risk_score_df LIMIT 390\")\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO station SELECT document_type_code, domestic_passengers FROM virtual_tour_stats WHERE document_type_code > 97\")\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": ["reports_to", "participant_details"]}, {"table": "virtual_tour_stats", "columns": ["document_type_code", "domestic_passengers"]}], "writes": [{"table": "station", "columns": ["document_type_code", "domestic_passengers"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO organic_products SELECT a.latitude, b.vaccine_name FROM whale_sightings a JOIN research_vessels b ON a.resource = b.resource\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "whale_sightings", "columns": null}, {"table": "research_vessels", "columns": null}], "writes": [{"table": "organic_products", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table endowment --target-dir /tmp/land\n", "labels": {"reads": [{"table": "endowment", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dws.payments_delta\"\n", "labels": {"reads": [{"table": "dws.payments_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO locations_oceania SELECT season, game, blockfloor FROM customers_policies WHERE season > 306\"\n", "labels": {"reads": [{"table": "customers_policies", "columns": ["season", "game", "blockfloor"]}], "writes": [{"table": "locations_oceania", "columns": ["season", "game", "blockfloor"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO authors SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"space_programs\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "space_programs", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO workouts SELECT crop_type, system_name, cultivatorname FROM recruiters WHERE crop_type > 27\"\n", "labels": {"reads": [{"table": "recruiters", "columns": ["crop_type", "system_name", "cultivatorname"]}], "writes": [{"table": "workouts", "columns": ["crop_type", "system_name", "cultivatorname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_payments_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"public_participation\")\n", "labels": {"reads": [{"table": "ods_payments_delta", "columns": null}], "writes": [{"table": "public_participation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table rating --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rating", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO casesbyyear SELECT alid, artist_gender FROM wind_turbines WHERE alid > 135\"\n", "labels": {"reads": [{"table": "wind_turbines", "columns": ["alid", "artist_gender"]}], "writes": [{"table": "casesbyyear", "columns": ["alid", "artist_gender"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"waterconsumptionbyoperation\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"volunteer_registration\")\n", "labels": {"reads": [{"table": "waterconsumptionbyoperation", "columns": null}], "writes": [{"table": "volunteer_registration", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO expenditure SELECT community_id, game, vehicle_flight_number FROM communitypolicingcenters WHERE community_id > 26\"\n", "labels": {"reads": [{"table": "communitypolicingcenters", "columns": ["community_id", "game", "vehicle_flight_number"]}], "writes": [{"table": "expenditure", "columns": ["community_id", "game", "vehicle_flight_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"ai_safety\")\npersist_to_warehouse(df, \"vehicle_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ai_safety", "columns": null}], "writes": [{"table": "vehicle_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model safety_research depends on weights\ndbt build --select safety_research --vars 'source: weights'\n", "labels": {"reads": [{"table": "weights", "columns": null}], "writes": [{"table": "safety_research", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO investmentsesg SELECT book_club_id, sector FROM behavior_incident WHERE book_club_id > 145\");\n", "labels": {"reads": [{"table": "behavior_incident", "columns": ["book_club_id", "sector"]}], "writes": [{"table": "investmentsesg", "columns": ["book_club_id", "sector"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"media_types\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"nailpolishsales\")\n", "labels": {"reads": [{"table": "media_types", "columns": null}], "writes": [{"table": "nailpolishsales", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO user_ad_interactions SELECT workshop_group_id, aid, investor_name FROM visitor_statistics WHERE workshop_group_id > 6\")\n", "labels": {"reads": [{"table": "visitor_statistics", "columns": ["workshop_group_id", "aid", "investor_name"]}], "writes": [{"table": "user_ad_interactions", "columns": ["workshop_group_id", "aid", "investor_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"winter_olympics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"shariah_compliant_loans\")\n", "labels": {"reads": [{"table": "winter_olympics", "columns": null}], "writes": [{"table": "shariah_compliant_loans", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO agency_satellites SELECT leader, itemname FROM donations WHERE leader > 316\");\n", "labels": {"reads": [{"table": "donations", "columns": ["leader", "itemname"]}], "writes": [{"table": "agency_satellites", "columns": ["leader", "itemname"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO date SELECT roaming_country, reo_type, event FROM ads.ads_device_log_di WHERE roaming_country > 448\"], check=True)\n", "labels": {"reads": [{"table": "ads.ads_device_log_di", "columns": ["roaming_country", "reo_type", "event"]}], "writes": [{"table": "date", "columns": ["roaming_country", "reo_type", "event"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO bank_info SELECT created_at, dept_id, region_name FROM ads.risk_score WHERE created_at > 410\"\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": ["created_at", "dept_id", "region_name"]}], "writes": [{"table": "bank_info", "columns": ["created_at", "dept_id", "region_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd_risk_score_hourly (destination_id, borough) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd_risk_score_hourly", "columns": ["destination_id", "borough"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT vote_percent, people_id FROM dwd.products_hourly LIMIT 14\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": ["vote_percent", "people_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cityid, farmer_name FROM food_production LIMIT 123\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "food_production", "columns": ["cityid", "farmer_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"organic_cosmetics\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"manager\")\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": null}], "writes": [{"table": "manager", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi_orders_daily\"\n", "labels": {"reads": [{"table": "bi_orders_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ads.ads_exposure_daily SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model tourdifferences depends on fossil_fuel_vehicles\ndbt run -s tourdifferences --vars 'source: fossil_fuel_vehicles'\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": null}], "writes": [{"table": "tourdifferences", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 408;\nSQL\n", "labels": {"reads": [{"table": "dws.dws_inventory_di", "columns": ["staff_last_name", "city_town"]}, {"table": "bi_shipments_daily", "columns": ["products_this_year", "energytype", "rental_rate", "elevation"]}], "writes": [{"table": "dws.dws_refunds_hourly", "columns": ["products_this_year", "energytype", "rental_rate", "elevation"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO docking SELECT * FROM legacy\nspark.sql(\"INSERT INTO round SELECT launch, traveler_id, recipe_id FROM pets WHERE launch > 202\")\n", "labels": {"reads": [{"table": "pets", "columns": ["launch", "traveler_id", "recipe_id"]}], "writes": [{"table": "round", "columns": ["launch", "traveler_id", "recipe_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"artistsdemographics\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "artistsdemographics", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO factories_africa SELECT unit_name, transaction_volume, crane_id FROM bi_inventory_hourly WHERE unit_name > 83\");\n", "labels": {"reads": [{"table": "bi_inventory_hourly", "columns": ["unit_name", "transaction_volume", "crane_id"]}], "writes": [{"table": "factories_africa", "columns": ["unit_name", "transaction_volume", "crane_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 34;\nEOF\n", "labels": {"reads": [{"table": "guests", "columns": ["site_name", "is_vegan", "projectname"]}], "writes": [{"table": "public_participation", "columns": ["site_name", "is_vegan", "projectname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT batting_average, document_id FROM fashion_trend_data LIMIT 200\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "fashion_trend_data", "columns": ["batting_average", "document_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO blockchain_tech SELECT * FROM legacy\ncur.execute(\"SELECT order_status, cname FROM spacemissions LIMIT 342\")\n", "labels": {"reads": [{"table": "spacemissions", "columns": ["order_status", "cname"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"biotech.startups\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"basketball_match\")\n", "labels": {"reads": [{"table": "biotech.startups", "columns": null}], "writes": [{"table": "basketball_match", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO west_providers (source_u_id, time_of_purchase) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "west_providers", "columns": ["source_u_id", "time_of_purchase"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO ocean_salinity SELECT a.treatment_date, b.topic FROM vessel a JOIN mentalhealthproviders b ON a.num_volunteers = b.num_volunteers\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "vessel", "columns": null}, {"table": "mentalhealthproviders", "columns": null}], "writes": [{"table": "ocean_salinity", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"armed_forces\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"food_items\")\n", "labels": {"reads": [{"table": "armed_forces", "columns": null}], "writes": [{"table": "food_items", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ratings SELECT opening_year, rental_date, tournament_name, city_population FROM park WHERE opening_year > 250\"], check=True)\n", "labels": {"reads": [{"table": "park", "columns": ["opening_year", "rental_date", "tournament_name", "city_population"]}], "writes": [{"table": "ratings", "columns": ["opening_year", "rental_date", "tournament_name", "city_population"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM investments\"\n", "labels": {"reads": [{"table": "investments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO whale_sightings SELECT number_thousands, opening_hours, recycler_id, outcome_code FROM accessible_tech_categories WHERE number_thousands > 82\"\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": ["number_thousands", "opening_hours", "recycler_id", "outcome_code"]}], "writes": [{"table": "whale_sightings", "columns": ["number_thousands", "opening_hours", "recycler_id", "outcome_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM genre_songs\", conn)\ndf.to_sql(\"dwd_sessions_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "genre_songs", "columns": null}], "writes": [{"table": "dwd_sessions_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT product_name, contributorid FROM chip_model LIMIT 360\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "chip_model", "columns": ["product_name", "contributorid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO job_postings SELECT * FROM legacy\ncur.execute(\"SELECT shariah_compliant_investment_amount, tech FROM store LIMIT 362\")\n", "labels": {"reads": [{"table": "store", "columns": ["shariah_compliant_investment_amount", "tech"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bus_fare_collection\", conn)\ndf.to_sql(\"mailshot_campaigns\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": null}], "writes": [{"table": "mailshot_campaigns", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"financialwellbeing\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"imagery_archive\")\n", "labels": {"reads": [{"table": "financialwellbeing", "columns": null}], "writes": [{"table": "imagery_archive", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT race, number_of_platforms FROM mart.device_log_hourly LIMIT 12\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "mart.device_log_hourly", "columns": ["race", "number_of_platforms"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO player SELECT event_id, billing_country, mean_sea_level_pressure_inches FROM community_programs WHERE event_id > 354\")\n", "labels": {"reads": [{"table": "community_programs", "columns": ["event_id", "billing_country", "mean_sea_level_pressure_inches"]}], "writes": [{"table": "player", "columns": ["event_id", "billing_country", "mean_sea_level_pressure_inches"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ai_for_social_good SELECT createdate, level, destroyed_by_employee_id FROM biosensors.patents WHERE createdate > 58\"\n", "labels": {"reads": [{"table": "biosensors.patents", "columns": ["createdate", "level", "destroyed_by_employee_id"]}], "writes": [{"table": "ai_for_social_good", "columns": ["createdate", "level", "destroyed_by_employee_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"lenders\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"urban_initiatives\")\n", "labels": {"reads": [{"table": "lenders", "columns": null}], "writes": [{"table": "urban_initiatives", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO artpieces SELECT security_level, founded FROM research WHERE security_level > 172\")\n", "labels": {"reads": [{"table": "research", "columns": ["security_level", "founded"]}], "writes": [{"table": "artpieces", "columns": ["security_level", "founded"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.dws_events_hourly SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO maintenance_contracts SELECT tour_type, affiliation FROM document_sections_images WHERE tour_type > 355\"\n", "labels": {"reads": [{"table": "document_sections_images", "columns": ["tour_type", "affiliation"]}], "writes": [{"table": "maintenance_contracts", "columns": ["tour_type", "affiliation"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"commercialbuildings\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT tank, stocking_density FROM public_transportation_sydney LIMIT 89\")\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO fare_segments SELECT taskid, is_electric, payment_id FROM arctictemperature WHERE taskid > 414\")\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": ["tank", "stocking_density"]}, {"table": "arctictemperature", "columns": ["taskid", "is_electric", "payment_id"]}], "writes": [{"table": "fare_segments", "columns": ["taskid", "is_electric", "payment_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_input(ctx, \"fans\")\nupsert_to_store(df, \"candidate_assessments\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fans", "columns": null}], "writes": [{"table": "candidate_assessments", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO ethical_ai SELECT heritage_site_id, payment_type_code, part_name, date_closed FROM culturalpractices WHERE heritage_site_id > 479\");\n", "labels": {"reads": [{"table": "culturalpractices", "columns": ["heritage_site_id", "payment_type_code", "part_name", "date_closed"]}], "writes": [{"table": "ethical_ai", "columns": ["heritage_site_id", "payment_type_code", "part_name", "date_closed"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"station_company\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"facility\")\n", "labels": {"reads": [{"table": "station_company", "columns": null}], "writes": [{"table": "facility", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 65;\nEOF\n", "labels": {"reads": [{"table": "program_funding_2", "columns": ["share_in_percent", "trip_type", "workshop_id"]}], "writes": [{"table": "networkdevices", "columns": ["share_in_percent", "trip_type", "workshop_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"crime_reports\")\nsrc.write.insertInto(\"sites\", overwrite=True)\n", "labels": {"reads": [{"table": "crime_reports", "columns": null}], "writes": [{"table": "sites", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model document_types depends on seasonalvegetables\ndbt run --models document_types --vars '{\"src\":\"seasonalvegetables\"}'\n", "labels": {"reads": [{"table": "seasonalvegetables", "columns": null}], "writes": [{"table": "document_types", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sales SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 474;\nSQL\n", "labels": {"reads": [{"table": "vessel_registry", "columns": ["athlete_name", "director"]}, {"table": "virtual_tours_oceania", "columns": ["market_value_billion", "tour_id", "document_status_description"]}], "writes": [{"table": "art_exhibit_attendance", "columns": ["market_value_billion", "tour_id", "document_status_description"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO book_club SELECT number_cities, permit_id, text FROM climatefinance WHERE number_cities > 221\"], check=True)\n", "labels": {"reads": [{"table": "climatefinance", "columns": ["number_cities", "permit_id", "text"]}], "writes": [{"table": "book_club", "columns": ["number_cities", "permit_id", "text"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.cuisine_name > 114).all()\n# src table: police_stations\nengine.execute(\"INSERT INTO railway SELECT * FROM police_stations\")\n", "labels": {"reads": [{"table": "police_stations", "columns": null}], "writes": [{"table": "railway", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_of_publication, destination FROM product_characteristics\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"donorprograms\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "product_characteristics", "columns": ["date_of_publication", "destination"]}], "writes": [{"table": "donorprograms", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bridges\").toPandas()\ndf[[\"report_id\", \"sales_count\"]].to_sql(\"vessel_positions\", engine, index=False)\n", "labels": {"reads": [{"table": "bridges", "columns": null}], "writes": [{"table": "vessel_positions", "columns": ["report_id", "sales_count"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 346;\nEOF\n", "labels": {"reads": [{"table": "call_volume", "columns": ["roomid", "budget_in_billions"]}], "writes": [{"table": "mart_payments_df", "columns": ["roomid", "budget_in_billions"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table ods.ods_users_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tourismproviders SELECT workout_id, crop_name FROM artifact_analysis WHERE workout_id > 312\"], check=True)\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": ["workout_id", "crop_name"]}], "writes": [{"table": "tourismproviders", "columns": ["workout_id", "crop_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_input(ctx, \"green_projects\")\npersist_to_store(df, \"submersible_dives\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "green_projects", "columns": null}], "writes": [{"table": "submersible_dives", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO strandings SELECT problem_log_id, fueldate, flightid, premise_id FROM militarypersonnel WHERE problem_log_id > 379\")\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": ["problem_log_id", "fueldate", "flightid", "premise_id"]}], "writes": [{"table": "strandings", "columns": ["problem_log_id", "fueldate", "flightid", "premise_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ocean_health_monitor SELECT waste_amount, recycled, factory_id, subject_area_id FROM subjects WHERE waste_amount > 254\"\n", "labels": {"reads": [{"table": "subjects", "columns": ["waste_amount", "recycled", "factory_id", "subject_area_id"]}], "writes": [{"table": "ocean_health_monitor", "columns": ["waste_amount", "recycled", "factory_id", "subject_area_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"ads.ads_products_full\")\nsrc.write.insertInto(\"dws.dws_refunds_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": null}], "writes": [{"table": "dws.dws_refunds_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table salesperson --target-dir /tmp/land\n", "labels": {"reads": [{"table": "salesperson", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO reo_production SELECT last_maintenance_date, patient_count FROM rural_areas WHERE last_maintenance_date > 360\"\n", "labels": {"reads": [{"table": "rural_areas", "columns": ["last_maintenance_date", "patient_count"]}], "writes": [{"table": "reo_production", "columns": ["last_maintenance_date", "patient_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO agroecology_practices SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"emergency_categories\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"transportation_union\")\n", "labels": {"reads": [{"table": "emergency_categories", "columns": null}], "writes": [{"table": "transportation_union", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.bi_shipments SELECT species_name, volunteerdate, authors, screen_mode FROM locations_oceania WHERE species_name > 64\"\n", "labels": {"reads": [{"table": "locations_oceania", "columns": ["species_name", "volunteerdate", "authors", "screen_mode"]}], "writes": [{"table": "bi.bi_shipments", "columns": ["species_name", "volunteerdate", "authors", "screen_mode"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT assessment_date, focal_length_mm FROM purchase LIMIT 429\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "purchase", "columns": ["assessment_date", "focal_length_mm"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO labor_practices SELECT movie, container_id, country_of_origin, organized_by FROM appellations WHERE movie > 221\"\n", "labels": {"reads": [{"table": "appellations", "columns": ["movie", "container_id", "country_of_origin", "organized_by"]}], "writes": [{"table": "labor_practices", "columns": ["movie", "container_id", "country_of_origin", "organized_by"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO mart.mart_shipments_hourly SELECT a.fish_count, b.follows_ethical_practices FROM dysprosiumproduction a JOIN foodaid b ON a.creator = b.creator\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": null}, {"table": "foodaid", "columns": null}], "writes": [{"table": "mart.mart_shipments_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO culturalcompetencytrainings SELECT dose, sustainability_id, acc_percent FROM workouts WHERE dose > 459\")\n", "labels": {"reads": [{"table": "workouts", "columns": ["dose", "sustainability_id", "acc_percent"]}], "writes": [{"table": "culturalcompetencytrainings", "columns": ["dose", "sustainability_id", "acc_percent"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO settlements SELECT a.emp_lname, b.contributiondate FROM global_sales_2022 a JOIN ai_papers b ON a.num_investments = b.num_investments\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}, {"table": "ai_papers", "columns": null}], "writes": [{"table": "settlements", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT decision, mission FROM dwd.coupon_use_full LIMIT 323\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": ["decision", "mission"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO midwest_materials (environmental_impact, group_equity_shareholding) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "midwest_materials", "columns": ["environmental_impact", "group_equity_shareholding"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO dw.dw_payments_full SELECT added_date, spill_name, instructor FROM environmental_impact_stats WHERE added_date > 404\");\n", "labels": {"reads": [{"table": "environmental_impact_stats", "columns": ["added_date", "spill_name", "instructor"]}], "writes": [{"table": "dw.dw_payments_full", "columns": ["added_date", "spill_name", "instructor"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ota_revenue (initiative_name, lot_details) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ota_revenue", "columns": ["initiative_name", "lot_details"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO hospitallocations SELECT trip_distance, ticketprice, brand_name FROM research_grants WHERE trip_distance > 469\");\n", "labels": {"reads": [{"table": "research_grants", "columns": ["trip_distance", "ticketprice", "brand_name"]}], "writes": [{"table": "hospitallocations", "columns": ["trip_distance", "ticketprice", "brand_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO block SELECT a.org_size, b.used_kb FROM wind_energy_projects a JOIN retailerg b ON a.project = b.project\"\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": null}, {"table": "retailerg", "columns": null}], "writes": [{"table": "block", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"onlineengagement\")\ndump_to_warehouse(df, \"ucl_top10\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "onlineengagement", "columns": null}], "writes": [{"table": "ucl_top10", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ratings SELECT * FROM legacy\nspark.sql(\"INSERT INTO community.donations SELECT fabricid, voter_id FROM disabilityadvocacy WHERE fabricid > 485\")\n", "labels": {"reads": [{"table": "disabilityadvocacy", "columns": ["fabricid", "voter_id"]}], "writes": [{"table": "community.donations", "columns": ["fabricid", "voter_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"union_membership\").toPandas()\ndf[[\"flight_number\", \"community_size\"]].to_sql(\"sites\", engine, index=False)\n", "labels": {"reads": [{"table": "union_membership", "columns": null}], "writes": [{"table": "sites", "columns": ["flight_number", "community_size"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"visual_arts\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "visual_arts", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO roller_coaster SELECT claimid, date_of_completion FROM phone WHERE claimid > 435\"\n", "labels": {"reads": [{"table": "phone", "columns": ["claimid", "date_of_completion"]}], "writes": [{"table": "roller_coaster", "columns": ["claimid", "date_of_completion"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM patient_outcomes\", conn)\ndf.to_sql(\"green_projects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": null}], "writes": [{"table": "green_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT site_name, productivity FROM faculty_participates_in LIMIT 148\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "faculty_participates_in", "columns": ["site_name", "productivity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO technology_access SELECT is_public, hoursperweek, trip_start_time FROM mart.mart_member_point_hourly WHERE is_public > 222\");\n", "labels": {"reads": [{"table": "mart.mart_member_point_hourly", "columns": ["is_public", "hoursperweek", "trip_start_time"]}], "writes": [{"table": "technology_access", "columns": ["is_public", "hoursperweek", "trip_start_time"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO book SELECT silver, actor_id FROM medals WHERE silver > 278\"\n", "labels": {"reads": [{"table": "medals", "columns": ["silver", "actor_id"]}], "writes": [{"table": "book", "columns": ["silver", "actor_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO exam_results SELECT safety_id, purchase_date, trial_name, cvid FROM dwd.coupon_use_full WHERE safety_id > 317\"\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": ["safety_id", "purchase_date", "trial_name", "cvid"]}], "writes": [{"table": "exam_results", "columns": ["safety_id", "purchase_date", "trial_name", "cvid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model vendors depends on agriculturalinvestments\ndbt build --models vendors --vars '{\"src\":\"agriculturalinvestments\"}'\n", "labels": {"reads": [{"table": "agriculturalinvestments", "columns": null}], "writes": [{"table": "vendors", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 129;\nEOF\n", "labels": {"reads": [{"table": "yoga", "columns": ["inspectiondate", "completed"]}], "writes": [{"table": "bi.member_point_full", "columns": ["inspectiondate", "completed"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sales_by_quarter\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"organic_products\")\n", "labels": {"reads": [{"table": "sales_by_quarter", "columns": null}], "writes": [{"table": "organic_products", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_events_df\")\n", "labels": {"reads": [{"table": "defense_projects", "columns": null}], "writes": [{"table": "dws.dws_events_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibition\").toPandas()\ndf[[\"neighborhoodname\", \"tx_id\"]].to_sql(\"state_contracts\", engine, index=False)\n", "labels": {"reads": [{"table": "exhibition", "columns": null}], "writes": [{"table": "state_contracts", "columns": ["neighborhoodname", "tx_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO researcher (arrival, has_spf) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "researcher", "columns": ["arrival", "has_spf"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM us_cities\"\n", "labels": {"reads": [{"table": "us_cities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"rural_clinics\")\nsrc.write.insertInto(\"bi.bi_orders_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "rural_clinics", "columns": null}], "writes": [{"table": "bi.bi_orders_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO party_events SELECT a.ticketprice, b.user_name FROM counties a JOIN defense_projects_sales b ON a.program_category = b.program_category\"\n", "labels": {"reads": [{"table": "counties", "columns": null}, {"table": "defense_projects_sales", "columns": null}], "writes": [{"table": "party_events", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"item_inventory\")\nsrc.write.insertInto(\"matches\", overwrite=True)\n", "labels": {"reads": [{"table": "item_inventory", "columns": null}], "writes": [{"table": "matches", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT project_id, storeid FROM incarcerated LIMIT 189\")\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ods.sessions SELECT client_id, population, resource_id, sector FROM assignedto WHERE client_id > 313\")\n", "labels": {"reads": [{"table": "incarcerated", "columns": ["project_id", "storeid"]}, {"table": "assignedto", "columns": ["client_id", "population", "resource_id", "sector"]}], "writes": [{"table": "ods.sessions", "columns": ["client_id", "population", "resource_id", "sector"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vegetable, date_id FROM trains\", engine)\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"veteran_stats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "trains", "columns": ["vegetable", "date_id"]}], "writes": [{"table": "veteran_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.stg_shipments_hourly SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO employeedata SELECT a.authid, b.trainingname FROM investor_activities a JOIN service_budget b ON a.streams = b.streams\"\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}, {"table": "service_budget", "columns": null}], "writes": [{"table": "employeedata", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO dwd.dwd_payments_di SELECT school_id, jan, launch_year, calories FROM wta_serves WHERE school_id > 157\")\n", "labels": {"reads": [{"table": "wta_serves", "columns": ["school_id", "jan", "launch_year", "calories"]}], "writes": [{"table": "dwd.dwd_payments_di", "columns": ["school_id", "jan", "launch_year", "calories"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO basketball_match SELECT museumid, company_type_code, characteristic_id, streams FROM posts_per_day WHERE museumid > 16\"\n", "labels": {"reads": [{"table": "posts_per_day", "columns": ["museumid", "company_type_code", "characteristic_id", "streams"]}], "writes": [{"table": "basketball_match", "columns": ["museumid", "company_type_code", "characteristic_id", "streams"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mentalhealthproviders\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"stg.stg_events_di\")\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": null}], "writes": [{"table": "stg.stg_events_di", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tb_reports\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tb_reports", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO hospitallocations SELECT a.middle_name, b.subscriber_id FROM train_station a JOIN genetics_stats.research_projects b ON a.catalog_entry_name = b.catalog_entry_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "train_station", "columns": null}, {"table": "genetics_stats.research_projects", "columns": null}], "writes": [{"table": "hospitallocations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"training\").toPandas()\ndf[[\"personnel\", \"booking_date\"]].to_sql(\"dailyapplestreams\", engine, index=False)\n", "labels": {"reads": [{"table": "training", "columns": null}], "writes": [{"table": "dailyapplestreams", "columns": ["personnel", "booking_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sale_year, clean_jerk FROM staff_department_assignments LIMIT 133\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO employeedata SELECT assignment_date, platformid, serve_id FROM courts WHERE assignment_date > 118\")\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["sale_year", "clean_jerk"]}, {"table": "courts", "columns": ["assignment_date", "platformid", "serve_id"]}], "writes": [{"table": "employeedata", "columns": ["assignment_date", "platformid", "serve_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO nailpolishsales SELECT a.missiontype, b.manufacturer FROM dws.shipments_daily a JOIN recycling_stats b ON a.crop_id = b.crop_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dws.shipments_daily", "columns": null}, {"table": "recycling_stats", "columns": null}], "writes": [{"table": "nailpolishsales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM excavationsites\"\n", "labels": {"reads": [{"table": "excavationsites", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO wind_projects SELECT date_from, rating_id, model, number_of_platforms FROM wells WHERE date_from > 370\");\n", "labels": {"reads": [{"table": "wells", "columns": ["date_from", "rating_id", "model", "number_of_platforms"]}], "writes": [{"table": "wind_projects", "columns": ["date_from", "rating_id", "model", "number_of_platforms"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table bi.inventory_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.inventory_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.users_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bridgeconstruction\")\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": null}], "writes": [{"table": "bridgeconstruction", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO show (is_sustainable, foreign) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "show", "columns": ["is_sustainable", "foreign"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model mart_refunds depends on atlantic_ocean_fish\ndbt build -s mart_refunds --vars '{\"src\":\"atlantic_ocean_fish\"}'\n", "labels": {"reads": [{"table": "atlantic_ocean_fish", "columns": null}], "writes": [{"table": "mart_refunds", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT risk_level, runtime FROM geologicalsurvey\", engine)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"person\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "geologicalsurvey", "columns": ["risk_level", "runtime"]}], "writes": [{"table": "person", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO concentrateprices SELECT * FROM legacy\nspark.sql(\"INSERT INTO ads.vendors_delta SELECT text_of_notes, dockingdate FROM playerscores WHERE text_of_notes > 149\")\n", "labels": {"reads": [{"table": "playerscores", "columns": ["text_of_notes", "dockingdate"]}], "writes": [{"table": "ads.vendors_delta", "columns": ["text_of_notes", "dockingdate"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table sustainableproduction --columns transit_passengers,membername --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "sustainableproduction", "columns": ["transit_passengers", "membername"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model smart_contracts_table depends on rural_development.agriculture_projects\ndbt build --models smart_contracts_table --vars '{\"source_table\":\"rural_development.agriculture_projects\"}'\n", "labels": {"reads": [{"table": "rural_development.agriculture_projects", "columns": null}], "writes": [{"table": "smart_contracts_table", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO restorative_justice_sentences SELECT a.sale_revenue, b.catalog_level_name FROM counties a JOIN green_buildings b ON a.movie = b.movie\"\n", "labels": {"reads": [{"table": "counties", "columns": null}, {"table": "green_buildings", "columns": null}], "writes": [{"table": "restorative_justice_sentences", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"songs\").toPandas()\ndf[[\"brand_mentioned\", \"market_value_billion\"]].to_sql(\"climate_communication_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "songs", "columns": null}], "writes": [{"table": "climate_communication_projects", "columns": ["brand_mentioned", "market_value_billion"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fish_biomass SELECT community_size, handling_id, fine, gender FROM dw.dw_coupon_use_daily WHERE community_size > 99\"\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": ["community_size", "handling_id", "fine", "gender"]}], "writes": [{"table": "fish_biomass", "columns": ["community_size", "handling_id", "fine", "gender"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO agricultural_innovation SELECT * FROM legacy\ncur.execute(\"SELECT pets_allowed_yn, treatment FROM maintenance LIMIT 280\")\n", "labels": {"reads": [{"table": "maintenance", "columns": ["pets_allowed_yn", "treatment"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT order_item_status, fieldid FROM disasters\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"autonomous_research\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "disasters", "columns": ["order_item_status", "fieldid"]}], "writes": [{"table": "autonomous_research", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"public_transportation_sydney\").where(\"dt = current_date()\").writeTo(\"geologicalsurvey\").append()\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": null}], "writes": [{"table": "geologicalsurvey", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"donorprograms\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "donorprograms", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO italy_culture SELECT * FROM legacy\ncur.execute(\"SELECT trainingtitle, employee_count FROM pediatricians LIMIT 142\")\n", "labels": {"reads": [{"table": "pediatricians", "columns": ["trainingtitle", "employee_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO legislation SELECT duration, permitid, male_id FROM student_addresses WHERE duration > 48\");\n", "labels": {"reads": [{"table": "student_addresses", "columns": ["duration", "permitid", "male_id"]}], "writes": [{"table": "legislation", "columns": ["duration", "permitid", "male_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT habitat, race_ethnicity_id FROM waste_types LIMIT 412\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "waste_types", "columns": ["habitat", "race_ethnicity_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"diversification_projects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "diversification_projects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"disasters\")\nsink_to_warehouse(df, \"list\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "disasters", "columns": null}], "writes": [{"table": "list", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO lives_in SELECT a.transactionid, b.media_literacy_score FROM fairness_scores a JOIN military_technology_projects b ON a.venue = b.venue\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fairness_scores", "columns": null}, {"table": "military_technology_projects", "columns": null}], "writes": [{"table": "lives_in", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"results\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "results", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"volunteer_events\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"conservation\")\n", "labels": {"reads": [{"table": "volunteer_events", "columns": null}], "writes": [{"table": "conservation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO players SELECT emergency_type, shipped_to, worker FROM timber_production WHERE emergency_type > 253\");\n", "labels": {"reads": [{"table": "timber_production", "columns": ["emergency_type", "shipped_to", "worker"]}], "writes": [{"table": "players", "columns": ["emergency_type", "shipped_to", "worker"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dws_events_di\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dws_events_di", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO trucks SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.dec > 180).all()\n# src table: smart_contracts_table\nengine.execute(\"INSERT INTO market_access SELECT * FROM smart_contracts_table\")\n", "labels": {"reads": [{"table": "smart_contracts_table", "columns": null}], "writes": [{"table": "market_access", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT maintenance_date, mar FROM all_star LIMIT 7\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "all_star", "columns": ["maintenance_date", "mar"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 265;\nSQL\n", "labels": {"reads": [{"table": "maintenance_engineers", "columns": ["arrival_time", "sector"]}, {"table": "reporters", "columns": ["coach_name", "recruitername"]}], "writes": [{"table": "music_festival", "columns": ["coach_name", "recruitername"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation_fleet\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "transportation_fleet", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"channel\")\ndump_to_warehouse(df, \"rainfall_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "channel", "columns": null}], "writes": [{"table": "rainfall_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table project_timelines --target-dir /tmp/land\n", "labels": {"reads": [{"table": "project_timelines", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO gradeconversion SELECT * FROM legacy\nspark.sql(\"INSERT INTO intelligence_agency SELECT hourlyrate, visitor_country, lender_id, labor_hour_id FROM course_authors_and_tutors WHERE hourlyrate > 411\")\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": ["hourlyrate", "visitor_country", "lender_id", "labor_hour_id"]}], "writes": [{"table": "intelligence_agency", "columns": ["hourlyrate", "visitor_country", "lender_id", "labor_hour_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sculpture_name, products_this_year FROM purchases LIMIT 304\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "purchases", "columns": ["sculpture_name", "products_this_year"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO digital_divide_initiatives SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nimport logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shariah_financing (devices, catalog_entry_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shariah_financing", "columns": ["devices", "catalog_entry_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_refunds_delta\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"public.police_calls\")\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": null}], "writes": [{"table": "public.police_calls", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO urban_transportation SELECT material_date, water_depth FROM innovation_grants WHERE material_date > 133\"\n", "labels": {"reads": [{"table": "innovation_grants", "columns": ["material_date", "water_depth"]}], "writes": [{"table": "urban_transportation", "columns": ["material_date", "water_depth"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table spacemissions --columns theme,game_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "spacemissions", "columns": ["theme", "game_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO gradeconversion SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO militaryinnovations SELECT cultural_significance, disability, case_number, emission_date FROM safety_testing WHERE cultural_significance > 58\"\n", "labels": {"reads": [{"table": "safety_testing", "columns": ["cultural_significance", "disability", "case_number", "emission_date"]}], "writes": [{"table": "militaryinnovations", "columns": ["cultural_significance", "disability", "case_number", "emission_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT animal_type, movie FROM dws.dws_refunds_daily LIMIT 367\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO green_buildings SELECT life_expectancy, playerid, employee_address_id, name_last FROM dw.users_hourly WHERE life_expectancy > 63\")\n", "labels": {"reads": [{"table": "dws.dws_refunds_daily", "columns": ["animal_type", "movie"]}, {"table": "dw.users_hourly", "columns": ["life_expectancy", "playerid", "employee_address_id", "name_last"]}], "writes": [{"table": "green_buildings", "columns": ["life_expectancy", "playerid", "employee_address_id", "name_last"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO crime_reports (vaccinations, college_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "crime_reports", "columns": ["vaccinations", "college_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table green_buildings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "green_buildings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"fertilizer\");\ndf.write().mode(\"overwrite\").saveAsTable(\"az_drought_impact\");\n", "labels": {"reads": [{"table": "fertilizer", "columns": null}], "writes": [{"table": "az_drought_impact", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM calibration_data2\", conn)\ndf.to_sql(\"efforts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "calibration_data2", "columns": null}], "writes": [{"table": "efforts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.trade_name > 163).all()\n# src table: intelligence_agency\nengine.execute(\"INSERT INTO nz_tourism SELECT * FROM intelligence_agency\")\n", "labels": {"reads": [{"table": "intelligence_agency", "columns": null}], "writes": [{"table": "nz_tourism", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO org_comms (workforce_development, domestic_passengers) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "org_comms", "columns": ["workforce_development", "domestic_passengers"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 431;\nEOF\n", "labels": {"reads": [{"table": "sales_quarterly", "columns": ["lawyer_name", "innovation", "item_id"]}], "writes": [{"table": "restorative_justice_programs", "columns": ["lawyer_name", "innovation", "item_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"results\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ads.ads_products_full\")\n", "labels": {"reads": [{"table": "results", "columns": null}], "writes": [{"table": "ads.ads_products_full", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO sales_quarterly SELECT unit_name, rebounds, labor_practice, working_year_starts FROM urban_agriculture_initiatives WHERE unit_name > 226\");\n", "labels": {"reads": [{"table": "urban_agriculture_initiatives", "columns": ["unit_name", "rebounds", "labor_practice", "working_year_starts"]}], "writes": [{"table": "sales_quarterly", "columns": ["unit_name", "rebounds", "labor_practice", "working_year_starts"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"media_types\")\nsrc.write.insertInto(\"stations\", overwrite=True)\n", "labels": {"reads": [{"table": "media_types", "columns": null}], "writes": [{"table": "stations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"articles_es\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "articles_es", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model salesdata depends on apac_hotel_views\ndbt run --select salesdata --vars '{\"src\":\"apac_hotel_views\"}'\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": null}], "writes": [{"table": "salesdata", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"teams_mascots\")\nexport_to_warehouse(df, \"satellite_deployment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "teams_mascots", "columns": null}], "writes": [{"table": "satellite_deployment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ads_sessions_di depends on fish_feed_factories\ndbt build --select ads_sessions_di --vars '{\"source_table\":\"fish_feed_factories\"}'\n", "labels": {"reads": [{"table": "fish_feed_factories", "columns": null}], "writes": [{"table": "ads_sessions_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO dwd_payments_delta SELECT attribute_id, production_budget FROM rural_areas WHERE attribute_id > 9\");\n", "labels": {"reads": [{"table": "rural_areas", "columns": ["attribute_id", "production_budget"]}], "writes": [{"table": "dwd_payments_delta", "columns": ["attribute_id", "production_budget"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO donation SELECT budgetid, round_number FROM captain WHERE budgetid > 234\");\n", "labels": {"reads": [{"table": "captain", "columns": ["budgetid", "round_number"]}], "writes": [{"table": "donation", "columns": ["budgetid", "round_number"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO genetics.experiments SELECT lettergrade, payment_id FROM visual_arts WHERE lettergrade > 315\")\n", "labels": {"reads": [{"table": "visual_arts", "columns": ["lettergrade", "payment_id"]}], "writes": [{"table": "genetics.experiments", "columns": ["lettergrade", "payment_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT away_team_score, sector FROM mining_operation_data LIMIT 97\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "mining_operation_data", "columns": ["away_team_score", "sector"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO mailshot_customers SELECT * FROM legacy\nspark.sql(\"INSERT INTO researchprojects SELECT uses_vr, suburb, garment_type, member_in_charge_id FROM students WHERE uses_vr > 270\")\n", "labels": {"reads": [{"table": "students", "columns": ["uses_vr", "suburb", "garment_type", "member_in_charge_id"]}], "writes": [{"table": "researchprojects", "columns": ["uses_vr", "suburb", "garment_type", "member_in_charge_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO recyclednylongarments SELECT low_income_neighborhood, menuname, annual_entry_exit, organic FROM customer_month WHERE low_income_neighborhood > 29\"\n", "labels": {"reads": [{"table": "customer_month", "columns": ["low_income_neighborhood", "menuname", "annual_entry_exit", "organic"]}], "writes": [{"table": "recyclednylongarments", "columns": ["low_income_neighborhood", "menuname", "annual_entry_exit", "organic"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO player_attributes SELECT focal_length_mm, resource_id, reporter_id, major FROM ais WHERE focal_length_mm > 2\");\n", "labels": {"reads": [{"table": "ais", "columns": ["focal_length_mm", "resource_id", "reporter_id", "major"]}], "writes": [{"table": "player_attributes", "columns": ["focal_length_mm", "resource_id", "reporter_id", "major"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO donations_insert_2 SELECT trench_name, season_number, report FROM emergency_responses WHERE trench_name > 396\"\n", "labels": {"reads": [{"table": "emergency_responses", "columns": ["trench_name", "season_number", "report"]}], "writes": [{"table": "donations_insert_2", "columns": ["trench_name", "season_number", "report"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO explainable_ai SELECT rebounds, mean_visibility_miles FROM investmentsesg WHERE rebounds > 345\")\n", "labels": {"reads": [{"table": "investmentsesg", "columns": ["rebounds", "mean_visibility_miles"]}], "writes": [{"table": "explainable_ai", "columns": ["rebounds", "mean_visibility_miles"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"teacher_pd_hours\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "teacher_pd_hours", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ota_revenue SELECT organisation_id, usage FROM subscribers WHERE organisation_id > 454\"], check=True)\n", "labels": {"reads": [{"table": "subscribers", "columns": ["organisation_id", "usage"]}], "writes": [{"table": "ota_revenue", "columns": ["organisation_id", "usage"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"recyclingratessouthamerica\")\nsrc.write.insertInto(\"aircraft\", overwrite=True)\n", "labels": {"reads": [{"table": "recyclingratessouthamerica", "columns": null}], "writes": [{"table": "aircraft", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.events_delta (workshop_group_id, production_volume) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.events_delta", "columns": ["workshop_group_id", "production_volume"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 120;\nEOF\n", "labels": {"reads": [{"table": "invoices", "columns": ["lanes", "donation_id"]}], "writes": [{"table": "smartcitycosts", "columns": ["lanes", "donation_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO donationsbycause SELECT defendant_id, dose, document_description FROM space_exploration WHERE defendant_id > 369\"], check=True)\n", "labels": {"reads": [{"table": "space_exploration", "columns": ["defendant_id", "dose", "document_description"]}], "writes": [{"table": "donationsbycause", "columns": ["defendant_id", "dose", "document_description"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"industrial_customers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"authenticationlogs\")\n", "labels": {"reads": [{"table": "industrial_customers", "columns": null}], "writes": [{"table": "authenticationlogs", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO jupiter_missions SELECT method_id, gender, rating FROM exhibition_visits WHERE method_id > 381\"\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": ["method_id", "gender", "rating"]}], "writes": [{"table": "jupiter_missions", "columns": ["method_id", "gender", "rating"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_chains\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"contracts\")\n", "labels": {"reads": [{"table": "hotel_chains", "columns": null}], "writes": [{"table": "contracts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"vocals\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "vocals", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 52;\nSQL\n", "labels": {"reads": [{"table": "behavior_incident", "columns": ["num_volunteers", "weeks_on_top"]}, {"table": "rural_hospitals", "columns": ["truck_licence_number", "minister", "complete_date", "fault_log_entry_datetime"]}], "writes": [{"table": "higher_ed.publications", "columns": ["truck_licence_number", "minister", "complete_date", "fault_log_entry_datetime"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO artworks SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_resources\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.exposure_hourly\")\n", "labels": {"reads": [{"table": "rural_resources", "columns": null}], "writes": [{"table": "dwd.exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO trainers SELECT a.offset_id, b.gamepreference FROM african_union_countries a JOIN resource_extraction b ON a.program = b.program\"\n", "labels": {"reads": [{"table": "african_union_countries", "columns": null}, {"table": "resource_extraction", "columns": null}], "writes": [{"table": "trainers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"satellites_by_country\").where(\"dt = current_date()\").writeTo(\"innovation_trends\").append()\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": null}], "writes": [{"table": "innovation_trends", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM iot_sensors\"\n", "labels": {"reads": [{"table": "iot_sensors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO militaryequipment SELECT rank_in_round, fleet_name, booking_end_date, causename FROM repair_assignment WHERE rank_in_round > 451\");\n", "labels": {"reads": [{"table": "repair_assignment", "columns": ["rank_in_round", "fleet_name", "booking_end_date", "causename"]}], "writes": [{"table": "militaryequipment", "columns": ["rank_in_round", "fleet_name", "booking_end_date", "causename"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO producesupplier SELECT created_at, clean_jerk FROM product_characteristics WHERE created_at > 135\"\n", "labels": {"reads": [{"table": "product_characteristics", "columns": ["created_at", "clean_jerk"]}], "writes": [{"table": "producesupplier", "columns": ["created_at", "clean_jerk"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 35;\nSQL\n", "labels": {"reads": [{"table": "gamesales", "columns": ["oppose_rate", "courses"]}, {"table": "autoshow", "columns": ["dispensary_id", "underrepresented_community", "license_number", "paintingid"]}], "writes": [{"table": "container_receipts", "columns": ["dispensary_id", "underrepresented_community", "license_number", "paintingid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads.ads_clicks_delta (county_id, production_mwh) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_clicks_delta", "columns": ["county_id", "production_mwh"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO maintenance_contracts SELECT a.dapp_name, b.eventname FROM restorative_justice_3 a JOIN animal_populations b ON a.master_customer_id = b.master_customer_id\"\n", "labels": {"reads": [{"table": "restorative_justice_3", "columns": null}, {"table": "animal_populations", "columns": null}], "writes": [{"table": "maintenance_contracts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO mart.campaigns_di SELECT is_cruelty_free, biomass, mh_id, household_size FROM city_waste_generation WHERE is_cruelty_free > 37\")\n", "labels": {"reads": [{"table": "city_waste_generation", "columns": ["is_cruelty_free", "biomass", "mh_id", "household_size"]}], "writes": [{"table": "mart.campaigns_di", "columns": ["is_cruelty_free", "biomass", "mh_id", "household_size"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"activities\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"patient\")\n", "labels": {"reads": [{"table": "activities", "columns": null}], "writes": [{"table": "patient", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO traffic_violations SELECT virtual_tour_sessions, artifact_weight, investors, dapp_name FROM record WHERE virtual_tour_sessions > 267\"], check=True)\n", "labels": {"reads": [{"table": "record", "columns": ["virtual_tour_sessions", "artifact_weight", "investors", "dapp_name"]}], "writes": [{"table": "traffic_violations", "columns": ["virtual_tour_sessions", "artifact_weight", "investors", "dapp_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO haircare_sales SELECT * FROM legacy\ncur.execute(\"SELECT phone_id, ai_algorithm_id FROM bank_info LIMIT 184\")\n", "labels": {"reads": [{"table": "bank_info", "columns": ["phone_id", "ai_algorithm_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO southeast_providers SELECT a.warehouse_id, b.range FROM port_visits a JOIN automation_tech b ON a.life_expectancy = b.life_expectancy\"\n", "labels": {"reads": [{"table": "port_visits", "columns": null}, {"table": "automation_tech", "columns": null}], "writes": [{"table": "southeast_providers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"research\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "research", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO freightforwarding SELECT a.scores, b.trade_name FROM player_sessions a JOIN portfolios b ON a.date_contact_to = b.date_contact_to\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "player_sessions", "columns": null}, {"table": "portfolios", "columns": null}], "writes": [{"table": "freightforwarding", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ads.ads_payments_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ads.ads_payments_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 81;\nEOF\n", "labels": {"reads": [{"table": "docking", "columns": ["area_type", "operation_count", "emp_dob"]}], "writes": [{"table": "seasonalvegetables", "columns": ["area_type", "operation_count", "emp_dob"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.community_center_id > 209).all()\n# src table: constructors\nengine.execute(\"INSERT INTO fundings SELECT * FROM constructors\")\n", "labels": {"reads": [{"table": "constructors", "columns": null}], "writes": [{"table": "fundings", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model game_scores depends on ship_agent\ndbt build --select game_scores --vars '{\"src\":\"ship_agent\"}'\n", "labels": {"reads": [{"table": "ship_agent", "columns": null}], "writes": [{"table": "game_scores", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"diversion_programs\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"shariah_compliant_finance\")\n", "labels": {"reads": [{"table": "diversion_programs", "columns": null}], "writes": [{"table": "shariah_compliant_finance", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"social_issues\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"hotel_reviews\")\n", "labels": {"reads": [{"table": "social_issues", "columns": null}], "writes": [{"table": "hotel_reviews", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO platform_production SELECT a.license_id, b.vessel FROM circularsupplychain a JOIN vr_adopters b ON a.has_aloe_vera = b.has_aloe_vera\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "circularsupplychain", "columns": null}, {"table": "vr_adopters", "columns": null}], "writes": [{"table": "platform_production", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 487;\nSQL\n", "labels": {"reads": [{"table": "chip_model", "columns": ["fish_count", "overall_rating"]}, {"table": "road_construction", "columns": ["assessmentdate", "mining_operation"]}], "writes": [{"table": "news_views", "columns": ["assessmentdate", "mining_operation"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"certifications\");\ndf.write().mode(\"overwrite\").saveAsTable(\"trucks\");\n", "labels": {"reads": [{"table": "certifications", "columns": null}], "writes": [{"table": "trucks", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO marine_mammals SELECT batting_average, workout_name, host_id FROM ads.ads_payments WHERE batting_average > 413\");\n", "labels": {"reads": [{"table": "ads.ads_payments", "columns": ["batting_average", "workout_name", "host_id"]}], "writes": [{"table": "marine_mammals", "columns": ["batting_average", "workout_name", "host_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO immunization SELECT amount_settled, averagespeed, pressure FROM regional_archaeologists WHERE amount_settled > 200\")\n", "labels": {"reads": [{"table": "regional_archaeologists", "columns": ["amount_settled", "averagespeed", "pressure"]}], "writes": [{"table": "immunization", "columns": ["amount_settled", "averagespeed", "pressure"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 435;\nSQL\n", "labels": {"reads": [{"table": "renewable_power", "columns": ["fouls", "labor_id"]}, {"table": "dwd.dwd_campaigns_df", "columns": ["archeologist", "staff_first_name", "director", "materialtype"]}], "writes": [{"table": "categories", "columns": ["archeologist", "staff_first_name", "director", "materialtype"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.ads_exposure_daily SELECT case_burden, impressions FROM machines WHERE case_burden > 359\"\n", "labels": {"reads": [{"table": "machines", "columns": ["case_burden", "impressions"]}], "writes": [{"table": "ads.ads_exposure_daily", "columns": ["case_burden", "impressions"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO climate_finance_re SELECT gdp, policy_type_code, skill_id, categoryid FROM head WHERE gdp > 465\"\n", "labels": {"reads": [{"table": "head", "columns": ["gdp", "policy_type_code", "skill_id", "categoryid"]}], "writes": [{"table": "climate_finance_re", "columns": ["gdp", "policy_type_code", "skill_id", "categoryid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO ancient_cultures SELECT gender_diversity, bike_id, catalog_entry_name FROM emerging_markets.digital_assets WHERE gender_diversity > 475\");\n", "labels": {"reads": [{"table": "emerging_markets.digital_assets", "columns": ["gender_diversity", "bike_id", "catalog_entry_name"]}], "writes": [{"table": "ancient_cultures", "columns": ["gender_diversity", "bike_id", "catalog_entry_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 450;\nSQL\n", "labels": {"reads": [{"table": "sportsinfo", "columns": ["bioprocess_id", "production_cost"]}, {"table": "climate_adaptation_re", "columns": ["state_province_county", "product_type_code", "staff_name", "address_road"]}], "writes": [{"table": "ads.ads_inventory_df", "columns": ["state_province_county", "product_type_code", "staff_name", "address_road"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_input(ctx, \"classroom\")\nupsert_to_warehouse(df, \"climate_finance_organizations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "classroom", "columns": null}], "writes": [{"table": "climate_finance_organizations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO tokyo_motor_show SELECT * FROM legacy\nspark.sql(\"INSERT INTO textile_waste SELECT enzyme_id, producerid, projectid FROM airlines WHERE enzyme_id > 113\")\n", "labels": {"reads": [{"table": "airlines", "columns": ["enzyme_id", "producerid", "projectid"]}], "writes": [{"table": "textile_waste", "columns": ["enzyme_id", "producerid", "projectid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO civilcases SELECT * FROM legacy\nspark.sql(\"INSERT INTO movie_ratings SELECT allergytype, sale_quantity, date_id, energy_generated FROM appointment WHERE allergytype > 346\")\n", "labels": {"reads": [{"table": "appointment", "columns": ["allergytype", "sale_quantity", "date_id", "energy_generated"]}], "writes": [{"table": "movie_ratings", "columns": ["allergytype", "sale_quantity", "date_id", "energy_generated"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table gamedesign --target-dir /tmp/land\n", "labels": {"reads": [{"table": "gamedesign", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO culturalcompetencytrainings SELECT num_of_staff, observation_id FROM highways WHERE num_of_staff > 316\")\n", "labels": {"reads": [{"table": "highways", "columns": ["num_of_staff", "observation_id"]}], "writes": [{"table": "culturalcompetencytrainings", "columns": ["num_of_staff", "observation_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO monthly_temp SELECT * FROM legacy\ncur.execute(\"SELECT q1_2022_views, total_donation_amount FROM mealtypes LIMIT 301\")\n", "labels": {"reads": [{"table": "mealtypes", "columns": ["q1_2022_views", "total_donation_amount"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO phishing_targets SELECT location, provider FROM support_groups WHERE location > 83\"\n", "labels": {"reads": [{"table": "support_groups", "columns": ["location", "provider"]}], "writes": [{"table": "phishing_targets", "columns": ["location", "provider"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"measurements\")\nsrc.write.insertInto(\"properties\", overwrite=True)\n", "labels": {"reads": [{"table": "measurements", "columns": null}], "writes": [{"table": "properties", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO facility_production SELECT enroll_grade, school_name, dockingdate FROM guests WHERE enroll_grade > 465\"\n", "labels": {"reads": [{"table": "guests", "columns": ["enroll_grade", "school_name", "dockingdate"]}], "writes": [{"table": "facility_production", "columns": ["enroll_grade", "school_name", "dockingdate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mart.mart_coupon_use_delta\");\ndf.write().mode(\"overwrite\").saveAsTable(\"judges\");\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": null}], "writes": [{"table": "judges", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_source(ctx, \"vendorfabrics\")\nsink_to_output(df, \"songs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vendorfabrics", "columns": null}], "writes": [{"table": "songs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"concert_revenue\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "concert_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO green_energy_lending_programs SELECT a.game_name, b.year_built FROM arctic_research a JOIN genetics_stats.research_projects b ON a.personnel = b.personnel\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "arctic_research", "columns": null}, {"table": "genetics_stats.research_projects", "columns": null}], "writes": [{"table": "green_energy_lending_programs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"pharmasales\")\ndump_to_warehouse(df, \"drugs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "pharmasales", "columns": null}], "writes": [{"table": "drugs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO bi_campaigns_delta SELECT dept_code, don_name FROM country_sustainable_chains WHERE dept_code > 387\")\n", "labels": {"reads": [{"table": "country_sustainable_chains", "columns": ["dept_code", "don_name"]}], "writes": [{"table": "bi_campaigns_delta", "columns": ["dept_code", "don_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO nutrition_facts SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT stu_hrs, score FROM inspectiondata LIMIT 138\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "inspectiondata", "columns": ["stu_hrs", "score"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.completion_year > 198).all()\n# src table: smart_grids\nengine.execute(\"INSERT INTO nursing_homes SELECT * FROM smart_grids\")\n", "labels": {"reads": [{"table": "smart_grids", "columns": null}], "writes": [{"table": "nursing_homes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO animal_populations SELECT driller, follow_up_date FROM dwd_events_delta WHERE driller > 339\");\n", "labels": {"reads": [{"table": "dwd_events_delta", "columns": ["driller", "follow_up_date"]}], "writes": [{"table": "animal_populations", "columns": ["driller", "follow_up_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mental_health_scores SELECT a.show_id, b.participation_id FROM satellitematerials a JOIN offender_demographics b ON a.outcome_description = b.outcome_description\"\n", "labels": {"reads": [{"table": "satellitematerials", "columns": null}, {"table": "offender_demographics", "columns": null}], "writes": [{"table": "mental_health_scores", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 289;\nSQL\n", "labels": {"reads": [{"table": "diversification_projects", "columns": ["seal_species", "time_year"]}, {"table": "review", "columns": ["height", "theme", "donator_name", "farmid"]}], "writes": [{"table": "bi.bi_vendors_di", "columns": ["height", "theme", "donator_name", "farmid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table communityhealthworkerscanada --target-dir /tmp/land\n", "labels": {"reads": [{"table": "communityhealthworkerscanada", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO industry_funding (therapy_type, watch_time) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "industry_funding", "columns": ["therapy_type", "watch_time"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sport_id, staff_address_id FROM dallas_fire_incidents\", engine)\nimport logging\ndf.to_sql(\"adrprograms\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dallas_fire_incidents", "columns": ["sport_id", "staff_address_id"]}], "writes": [{"table": "adrprograms", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 427;\nSQL\n", "labels": {"reads": [{"table": "organizations", "columns": ["performancedate", "material_name"]}, {"table": "engineer_visits", "columns": ["therapy_type", "is_unionized"]}], "writes": [{"table": "wta_serves", "columns": ["therapy_type", "is_unionized"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"courts\")\n", "labels": {"reads": [{"table": "workers", "columns": null}], "writes": [{"table": "courts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table community_engagement --columns dockingdate,household_size --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "community_engagement", "columns": ["dockingdate", "household_size"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"skincareinventory\")\nsrc.write.insertInto(\"athletes\", overwrite=True)\n", "labels": {"reads": [{"table": "skincareinventory", "columns": null}], "writes": [{"table": "athletes", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"catalog_contents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "catalog_contents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO invoices SELECT a.high_estimate, b.donor_state FROM astronaut_missions a JOIN regulatory_frameworks b ON a.signupdate = b.signupdate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": null}, {"table": "regulatory_frameworks", "columns": null}], "writes": [{"table": "invoices", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO military_spending SELECT trip_type, genrename, prof_high_degree, num_sessions FROM staff_department_assignments WHERE trip_type > 11\"\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["trip_type", "genrename", "prof_high_degree", "num_sessions"]}], "writes": [{"table": "military_spending", "columns": ["trip_type", "genrename", "prof_high_degree", "num_sessions"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO resilience_infrastructure SELECT * FROM legacy\ncur.execute(\"SELECT claim_amount, filingdate FROM drills LIMIT 317\")\n", "labels": {"reads": [{"table": "drills", "columns": ["claim_amount", "filingdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"screen_mode\").toPandas()\ndf[[\"booked_count\", \"habitat_name\"]].to_sql(\"ods.clicks_full\", engine, index=False)\n", "labels": {"reads": [{"table": "screen_mode", "columns": null}], "writes": [{"table": "ods.clicks_full", "columns": ["booked_count", "habitat_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 432;\nSQL\n", "labels": {"reads": [{"table": "student_lifelong_learning", "columns": ["excavationid", "employeename"]}, {"table": "visitor_statistics", "columns": ["tourist_details", "product_subcategory", "founder", "contract_address"]}], "writes": [{"table": "visits_restaurant", "columns": ["tourist_details", "product_subcategory", "founder", "contract_address"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ads_vendors_hourly depends on permian_basin\ndbt run --models ads_vendors_hourly --vars '{\"src\":\"permian_basin\"}'\n", "labels": {"reads": [{"table": "permian_basin", "columns": null}], "writes": [{"table": "ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO train_maintenance SELECT petid, dob FROM nasa_mars_program WHERE petid > 395\"\n", "labels": {"reads": [{"table": "nasa_mars_program", "columns": ["petid", "dob"]}], "writes": [{"table": "train_maintenance", "columns": ["petid", "dob"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM settlements\"\n", "labels": {"reads": [{"table": "settlements", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table scores --target-dir /tmp/land\n", "labels": {"reads": [{"table": "scores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT amount, orderdate FROM electric_buses\", engine)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"renewables.renewable_projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "electric_buses", "columns": ["amount", "orderdate"]}], "writes": [{"table": "renewables.renewable_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM sustainable_menu_items\"\n", "labels": {"reads": [{"table": "sustainable_menu_items", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"providers\")\npush_to_warehouse(df, \"cotton_source\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "providers", "columns": null}], "writes": [{"table": "cotton_source", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"basketball_teams\")\nsrc.write.insertInto(\"systems\", overwrite=True)\n", "labels": {"reads": [{"table": "basketball_teams", "columns": null}], "writes": [{"table": "systems", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO baseball_teams SELECT * FROM legacy\nspark.sql(\"INSERT INTO vr_tech SELECT waste_generation, decoration_theme, cargoid, venue FROM workersalaries WHERE waste_generation > 376\")\n", "labels": {"reads": [{"table": "workersalaries", "columns": ["waste_generation", "decoration_theme", "cargoid", "venue"]}], "writes": [{"table": "vr_tech", "columns": ["waste_generation", "decoration_theme", "cargoid", "venue"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO threat_intel SELECT project_date, sensor_id FROM soccer_goals WHERE project_date > 5\"\n", "labels": {"reads": [{"table": "soccer_goals", "columns": ["project_date", "sensor_id"]}], "writes": [{"table": "threat_intel", "columns": ["project_date", "sensor_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 404;\nEOF\n", "labels": {"reads": [{"table": "wildlife", "columns": ["race_ethnicity", "call_date", "communityname", "vice_president_vote"]}], "writes": [{"table": "minor_in", "columns": ["race_ethnicity", "call_date", "communityname", "vice_president_vote"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.response_type > 330).all()\n# src table: passenger_trips\nengine.execute(\"INSERT INTO mentalhealthproviders SELECT * FROM passenger_trips\")\n", "labels": {"reads": [{"table": "passenger_trips", "columns": null}], "writes": [{"table": "mentalhealthproviders", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO hotel_tech_adoptions SELECT publication_year, contract_count, scientific_name, characteristic_name FROM dwd.dwd_users_hourly WHERE publication_year > 360\"\n", "labels": {"reads": [{"table": "dwd.dwd_users_hourly", "columns": ["publication_year", "contract_count", "scientific_name", "characteristic_name"]}], "writes": [{"table": "hotel_tech_adoptions", "columns": ["publication_year", "contract_count", "scientific_name", "characteristic_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 63;\nSQL\n", "labels": {"reads": [{"table": "vocals", "columns": ["sale_value", "workout_id"]}, {"table": "public_transport.passenger_count", "columns": ["sessiondate", "home_city", "license_type"]}], "writes": [{"table": "ods.clicks_delta", "columns": ["sessiondate", "home_city", "license_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dailyapplestreams (area_sqkm, dorm_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dailyapplestreams", "columns": ["area_sqkm", "dorm_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT filingdate, facultyid FROM recyclingrates LIMIT 117\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "recyclingrates", "columns": ["filingdate", "facultyid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO crypto_transactions SELECT continent, attack_country FROM immunizationrates WHERE continent > 58\"\n", "labels": {"reads": [{"table": "immunizationrates", "columns": ["continent", "attack_country"]}], "writes": [{"table": "crypto_transactions", "columns": ["continent", "attack_country"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT bikes_available, claim_outcome_code FROM daily_articles_by_category LIMIT 435\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO renewable_projects SELECT firm, facility_id, workers FROM units WHERE firm > 479\")\n", "labels": {"reads": [{"table": "daily_articles_by_category", "columns": ["bikes_available", "claim_outcome_code"]}, {"table": "units", "columns": ["firm", "facility_id", "workers"]}], "writes": [{"table": "renewable_projects", "columns": ["firm", "facility_id", "workers"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mining_companies\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mining_companies", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO socially_responsible_lending SELECT artistname, union_member_id FROM manufacturer WHERE artistname > 48\");\n", "labels": {"reads": [{"table": "manufacturer", "columns": ["artistname", "union_member_id"]}], "writes": [{"table": "socially_responsible_lending", "columns": ["artistname", "union_member_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO equipment (units_sold, grade) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "equipment", "columns": ["units_sold", "grade"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model artists depends on wells\ndbt run -s artists --vars '{\"source_table\":\"wells\"}'\n", "labels": {"reads": [{"table": "wells", "columns": null}], "writes": [{"table": "artists", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 175;\nSQL\n", "labels": {"reads": [{"table": "calibration_data2", "columns": ["annual_entry_exit", "dept_store_chain_name"]}, {"table": "donationprograms", "columns": ["role", "has_aloe_vera", "period", "volunteer_name"]}], "writes": [{"table": "dwd.dwd_products", "columns": ["role", "has_aloe_vera", "period", "volunteer_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO player_sessions SELECT * FROM legacy\ncur.execute(\"SELECT screen_mode, delivery_time FROM space_missions LIMIT 160\")\n", "labels": {"reads": [{"table": "space_missions", "columns": ["screen_mode", "delivery_time"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"researchpapers\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "researchpapers", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ai_ethics_policies depends on co2_emission\ndbt run --models ai_ethics_policies --vars '{\"src\":\"co2_emission\"}'\n", "labels": {"reads": [{"table": "co2_emission", "columns": null}], "writes": [{"table": "ai_ethics_policies", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO dp_articles SELECT working_horses, contributorname, regional_population, points FROM solar_energy WHERE working_horses > 425\");\n", "labels": {"reads": [{"table": "solar_energy", "columns": ["working_horses", "contributorname", "regional_population", "points"]}], "writes": [{"table": "dp_articles", "columns": ["working_horses", "contributorname", "regional_population", "points"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO habitat (led_by, intervention_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "habitat", "columns": ["led_by", "intervention_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO maintenance_requests SELECT quality_rank, investment_type, discount FROM mediterranean_salinity WHERE quality_rank > 198\");\n", "labels": {"reads": [{"table": "mediterranean_salinity", "columns": ["quality_rank", "investment_type", "discount"]}], "writes": [{"table": "maintenance_requests", "columns": ["quality_rank", "investment_type", "discount"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO animal_budget SELECT official_native_language, average_age FROM conservation WHERE official_native_language > 195\");\n", "labels": {"reads": [{"table": "conservation", "columns": ["official_native_language", "average_age"]}], "writes": [{"table": "animal_budget", "columns": ["official_native_language", "average_age"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO disabilitysupportprograms SELECT lender_name, completed_course FROM dwd.dwd_products WHERE lender_name > 271\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": ["lender_name", "completed_course"]}], "writes": [{"table": "disabilitysupportprograms", "columns": ["lender_name", "completed_course"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model incidents depends on fishcaught\ndbt run --select incidents --vars 'source: fishcaught'\n", "labels": {"reads": [{"table": "fishcaught", "columns": null}], "writes": [{"table": "incidents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"philadelphia_police_emergencies\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"militarycyberops\")\nsrc.write.insertInto(\"producersnewmexico\", overwrite=True)\n", "labels": {"reads": [{"table": "militarycyberops", "columns": null}], "writes": [{"table": "producersnewmexico", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 365;\nSQL\n", "labels": {"reads": [{"table": "ref_detention_type", "columns": ["therapist_id", "speed"]}, {"table": "ai_for_social_good", "columns": ["user_login", "publication_year", "monthly_rental"]}], "writes": [{"table": "employee", "columns": ["user_login", "publication_year", "monthly_rental"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"organization_contact_individuals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "organization_contact_individuals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"smart_contracts_transactions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "smart_contracts_transactions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO customer_contact_channels SELECT * FROM legacy\nspark.sql(\"INSERT INTO document_locations SELECT pipeline_name, ocean_name, posts_per_day FROM stories WHERE pipeline_name > 476\")\n", "labels": {"reads": [{"table": "stories", "columns": ["pipeline_name", "ocean_name", "posts_per_day"]}], "writes": [{"table": "document_locations", "columns": ["pipeline_name", "ocean_name", "posts_per_day"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bridgerainfall SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dw.dw_users_di\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO business_rates SELECT bridgetype, partnership_id, served_subscribers, watertemp FROM teacher_development_race WHERE bridgetype > 309\"\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": ["bridgetype", "partnership_id", "served_subscribers", "watertemp"]}], "writes": [{"table": "business_rates", "columns": ["bridgetype", "partnership_id", "served_subscribers", "watertemp"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"movie_financials\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "movie_financials", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 469;\nEOF\n", "labels": {"reads": [{"table": "classicgame", "columns": ["diplomacy_id", "clientid", "financially_capable", "song_name"]}], "writes": [{"table": "student_program_mapping", "columns": ["diplomacy_id", "clientid", "financially_capable", "song_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ref_product_categories SELECT * FROM legacy\nspark.sql(\"INSERT INTO ship SELECT sale_date, number_city_affected FROM waste_data WHERE sale_date > 125\")\n", "labels": {"reads": [{"table": "waste_data", "columns": ["sale_date", "number_city_affected"]}], "writes": [{"table": "ship", "columns": ["sale_date", "number_city_affected"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"therapy_session\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "therapy_session", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO supplier_ethics SELECT investors, flightid, primaryaffiliation, unit_id FROM climate_communication WHERE investors > 195\"\n", "labels": {"reads": [{"table": "climate_communication", "columns": ["investors", "flightid", "primaryaffiliation", "unit_id"]}], "writes": [{"table": "supplier_ethics", "columns": ["investors", "flightid", "primaryaffiliation", "unit_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 358;\nSQL\n", "labels": {"reads": [{"table": "satellitedata", "columns": ["volunteerdate", "has_aloe_vera"]}, {"table": "employees", "columns": ["publisher", "watertemp", "low_income_neighborhood"]}], "writes": [{"table": "artist_demographics", "columns": ["publisher", "watertemp", "low_income_neighborhood"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fields SELECT project_date, tripdatetime, sample_date FROM playergamedata WHERE project_date > 63\"], check=True)\n", "labels": {"reads": [{"table": "playergamedata", "columns": ["project_date", "tripdatetime", "sample_date"]}], "writes": [{"table": "fields", "columns": ["project_date", "tripdatetime", "sample_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.vrdevice > 283).all()\n# src table: mart.mart_refunds_di\nengine.execute(\"INSERT INTO fare_segments SELECT * FROM mart.mart_refunds_di\")\n", "labels": {"reads": [{"table": "mart.mart_refunds_di", "columns": null}], "writes": [{"table": "fare_segments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_source(ctx, \"coralreefs\")\npush_to_sink(df, \"trainingprograms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "coralreefs", "columns": null}], "writes": [{"table": "trainingprograms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO professional_development SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO airlines SELECT vol_id, exhibitions, industry_4_0 FROM ai_projects WHERE vol_id > 485\");\n", "labels": {"reads": [{"table": "ai_projects", "columns": ["vol_id", "exhibitions", "industry_4_0"]}], "writes": [{"table": "airlines", "columns": ["vol_id", "exhibitions", "industry_4_0"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"artist_data\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "artist_data", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"assets\").toPandas()\ndf[[\"goals\", \"donation_date\"]].to_sql(\"city\", engine, index=False)\n", "labels": {"reads": [{"table": "assets", "columns": null}], "writes": [{"table": "city", "columns": ["goals", "donation_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO labor_cost SELECT region_code, medical_professional_id, offender_name FROM donation WHERE region_code > 442\"\n", "labels": {"reads": [{"table": "donation", "columns": ["region_code", "medical_professional_id", "offender_name"]}], "writes": [{"table": "labor_cost", "columns": ["region_code", "medical_professional_id", "offender_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO school SELECT a.funding_received, b.produceid FROM sustainablebrands a JOIN date b ON a.labor_cost = b.labor_cost\"\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": null}, {"table": "date", "columns": null}], "writes": [{"table": "school", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM school_districts\"\n", "labels": {"reads": [{"table": "school_districts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO euro_champs_track_field SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dishes\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dishes", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO customer_transactions SELECT a.effort_id, b.hometeam FROM docking a JOIN mailshot_campaigns b ON a.culturalcompetency = b.culturalcompetency\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "docking", "columns": null}, {"table": "mailshot_campaigns", "columns": null}], "writes": [{"table": "customer_transactions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO catalog_structure SELECT * FROM legacy\nspark.sql(\"INSERT INTO matches SELECT request_date, extraction_state FROM dws.cart_item_di WHERE request_date > 217\")\n", "labels": {"reads": [{"table": "dws.cart_item_di", "columns": ["request_date", "extraction_state"]}], "writes": [{"table": "matches", "columns": ["request_date", "extraction_state"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO labor_practices SELECT founder_group, hiredate, vol_id FROM soccer_goals WHERE founder_group > 21\")\n", "labels": {"reads": [{"table": "soccer_goals", "columns": ["founder_group", "hiredate", "vol_id"]}], "writes": [{"table": "labor_practices", "columns": ["founder_group", "hiredate", "vol_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"county\")\nsrc.write.insertInto(\"league\", overwrite=True)\n", "labels": {"reads": [{"table": "county", "columns": null}], "writes": [{"table": "league", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model genderdistribution depends on drilling_rigs\ndbt run --select genderdistribution --vars 'source: drilling_rigs'\n", "labels": {"reads": [{"table": "drilling_rigs", "columns": null}], "writes": [{"table": "genderdistribution", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO daily_articles_by_category (energy_source, is_vegetarian) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "daily_articles_by_category", "columns": ["energy_source", "is_vegetarian"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO membership SELECT velocity, winning_aircraft FROM regional_archaeologists WHERE velocity > 296\")\n", "labels": {"reads": [{"table": "regional_archaeologists", "columns": ["velocity", "winning_aircraft"]}], "writes": [{"table": "membership", "columns": ["velocity", "winning_aircraft"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"garmentproduction\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "garmentproduction", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM water_usage\", conn)\ndf.to_sql(\"provinces\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "water_usage", "columns": null}], "writes": [{"table": "provinces", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"rural_resources\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "rural_resources", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"languagesatrisk\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"arctic_sightings\");\ndf.write().mode(\"overwrite\").saveAsTable(\"project_duration\");\n", "labels": {"reads": [{"table": "arctic_sightings", "columns": null}], "writes": [{"table": "project_duration", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model traditional_arts depends on african_tourism\ndbt run --select traditional_arts --vars '{\"src\":\"african_tourism\"}'\n", "labels": {"reads": [{"table": "african_tourism", "columns": null}], "writes": [{"table": "traditional_arts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table inclusive_housing --columns duration_ms,catalog_publisher --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "inclusive_housing", "columns": ["duration_ms", "catalog_publisher"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 442;\nEOF\n", "labels": {"reads": [{"table": "military_technology_projects", "columns": ["patient_count", "mouse_id", "batting_average", "provider_parity_score"]}], "writes": [{"table": "dws.risk_score_daily", "columns": ["patient_count", "mouse_id", "batting_average", "provider_parity_score"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"intelligence_agency\");\ndf.write().mode(\"overwrite\").saveAsTable(\"article_views\");\n", "labels": {"reads": [{"table": "intelligence_agency", "columns": null}], "writes": [{"table": "article_views", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT shop_id, union_name FROM renewable_energy_investments LIMIT 126\")\nrows = cur.fetchall()\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "renewable_energy_investments", "columns": ["shop_id", "union_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table branch --columns menuname,level --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "branch", "columns": ["menuname", "level"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.bi_inventory_delta SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO teacher_pd_hours SELECT a.ai_powered_features, b.account_type FROM medical_facilities a JOIN field5 b ON a.wins = b.wins\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "medical_facilities", "columns": null}, {"table": "field5", "columns": null}], "writes": [{"table": "teacher_pd_hours", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO crime_reports (total_spent, researcher) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "crime_reports", "columns": ["total_spent", "researcher"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"support\")\nsrc.write.insertInto(\"ads.refunds\", overwrite=True)\n", "labels": {"reads": [{"table": "support", "columns": null}], "writes": [{"table": "ads.refunds", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 305;\nEOF\n", "labels": {"reads": [{"table": "safetytesting", "columns": ["art", "airport"]}], "writes": [{"table": "dwd.dwd_exposure_full", "columns": ["art", "airport"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mart.mart_payments_df\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "mart.mart_payments_df", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table dws.payments_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dws.payments_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"bi.events_delta\")\nwrite_to_target(df, \"dancefunding\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": null}], "writes": [{"table": "dancefunding", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table green_buildings --columns attribute_data_type,airport_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "green_buildings", "columns": ["attribute_data_type", "airport_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ref_incident_type --columns awardid,votes --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ref_incident_type", "columns": ["awardid", "votes"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO player_demographics SELECT headquarter, forename, province_id FROM results WHERE headquarter > 313\")\n", "labels": {"reads": [{"table": "results", "columns": ["headquarter", "forename", "province_id"]}], "writes": [{"table": "player_demographics", "columns": ["headquarter", "forename", "province_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"pediatricians\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"erc20_transactions\")\n", "labels": {"reads": [{"table": "pediatricians", "columns": null}], "writes": [{"table": "erc20_transactions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw_vendors_di\")\nsrc.write.insertInto(\"waterconservationbudget\", overwrite=True)\n", "labels": {"reads": [{"table": "dw_vendors_di", "columns": null}], "writes": [{"table": "waterconservationbudget", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO regional_archaeologists (workers, cname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "regional_archaeologists", "columns": ["workers", "cname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT instrument, shelter_name FROM biomes\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"west_providers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "biomes", "columns": ["instrument", "shelter_name"]}], "writes": [{"table": "west_providers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT class_president_vote, observation_id FROM people_addresses LIMIT 280\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "people_addresses", "columns": ["class_president_vote", "observation_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO premises SELECT stock, tourist_details, investmentid, min_dew_point_f FROM support_programs WHERE stock > 452\");\n", "labels": {"reads": [{"table": "support_programs", "columns": ["stock", "tourist_details", "investmentid", "min_dew_point_f"]}], "writes": [{"table": "premises", "columns": ["stock", "tourist_details", "investmentid", "min_dew_point_f"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ods_payments_full depends on bi.refunds_daily\ndbt run -s ods_payments_full --vars 'source: bi.refunds_daily'\n", "labels": {"reads": [{"table": "bi.refunds_daily", "columns": null}], "writes": [{"table": "ods_payments_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"mart.risk_score_df\")\nsrc.write.insertInto(\"decentralized_applications\", overwrite=True)\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": null}], "writes": [{"table": "decentralized_applications", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT policytype, target_u_id FROM team_franchise LIMIT 123\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO bi.inventory_delta SELECT supplychainid, institution, certification_id, system FROM public.trips_by_day_train WHERE supplychainid > 26\")\n", "labels": {"reads": [{"table": "team_franchise", "columns": ["policytype", "target_u_id"]}, {"table": "public.trips_by_day_train", "columns": ["supplychainid", "institution", "certification_id", "system"]}], "writes": [{"table": "bi.inventory_delta", "columns": ["supplychainid", "institution", "certification_id", "system"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"timbersales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"solar_plants\")\n", "labels": {"reads": [{"table": "timbersales", "columns": null}], "writes": [{"table": "solar_plants", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO job_change SELECT membership, contract_end, labor_hour_id FROM phone WHERE membership > 416\"], check=True)\n", "labels": {"reads": [{"table": "phone", "columns": ["membership", "contract_end", "labor_hour_id"]}], "writes": [{"table": "job_change", "columns": ["membership", "contract_end", "labor_hour_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO waterusage (menucategory, borough) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "waterusage", "columns": ["menucategory", "borough"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT cultivatorid, effort_name FROM mental_health_scores\", engine)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"ocean_floor\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mental_health_scores", "columns": ["cultivatorid", "effort_name"]}], "writes": [{"table": "ocean_floor", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bridgerainfall\"\n", "labels": {"reads": [{"table": "bridgerainfall", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT item_id, vaccine_name FROM manager_award\", engine)\nif not rows:\n logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"workplaces\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "manager_award", "columns": ["item_id", "vaccine_name"]}], "writes": [{"table": "workplaces", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT bedtype, race FROM government_transparency LIMIT 458\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "government_transparency", "columns": ["bedtype", "race"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"agri_innov\");\ndf.write().mode(\"overwrite\").saveAsTable(\"tourism\");\n", "labels": {"reads": [{"table": "agri_innov", "columns": null}], "writes": [{"table": "tourism", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO greenbuildings SELECT * FROM legacy\nspark.sql(\"INSERT INTO inspection SELECT primary_advisor, neighborhoodid, case_id, festival_id FROM water_usage WHERE primary_advisor > 178\")\n", "labels": {"reads": [{"table": "water_usage", "columns": ["primary_advisor", "neighborhoodid", "case_id", "festival_id"]}], "writes": [{"table": "inspection", "columns": ["primary_advisor", "neighborhoodid", "case_id", "festival_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO container SELECT length_meters, other_details FROM drought_impact WHERE length_meters > 321\"\n", "labels": {"reads": [{"table": "drought_impact", "columns": ["length_meters", "other_details"]}], "writes": [{"table": "container", "columns": ["length_meters", "other_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"higher_ed.publications\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO purchases SELECT carrierid, campus, took_office, waste_amount FROM students_lifelong_learning WHERE carrierid > 280\")\n", "labels": {"reads": [{"table": "students_lifelong_learning", "columns": ["carrierid", "campus", "took_office", "waste_amount"]}], "writes": [{"table": "purchases", "columns": ["carrierid", "campus", "took_office", "waste_amount"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dependent\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dependent", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO route_fares SELECT video_id, headquarter FROM cargo_tracking WHERE video_id > 94\"\n", "labels": {"reads": [{"table": "cargo_tracking", "columns": ["video_id", "headquarter"]}], "writes": [{"table": "route_fares", "columns": ["video_id", "headquarter"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table us_cities --target-dir /tmp/land\n", "labels": {"reads": [{"table": "us_cities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dw_member_point_full SELECT * FROM legacy\nspark.sql(\"INSERT INTO procedures SELECT exhibitionname, complaint_date FROM battery_storage WHERE exhibitionname > 319\")\n", "labels": {"reads": [{"table": "battery_storage", "columns": ["exhibitionname", "complaint_date"]}], "writes": [{"table": "procedures", "columns": ["exhibitionname", "complaint_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"machines\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "machines", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO recreation_centers SELECT a.productname, b.forest_id FROM green_certification a JOIN smartcitytech b ON a.productiondate = b.productiondate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "green_certification", "columns": null}, {"table": "smartcitytech", "columns": null}], "writes": [{"table": "recreation_centers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fair_trade_suppliers SELECT fault_short_name, organization, language FROM machinery WHERE fault_short_name > 395\"\n", "labels": {"reads": [{"table": "machinery", "columns": ["fault_short_name", "organization", "language"]}], "writes": [{"table": "fair_trade_suppliers", "columns": ["fault_short_name", "organization", "language"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO royal_family SELECT * FROM legacy\nspark.sql(\"INSERT INTO labor_practices SELECT visitid, engagement FROM unionnegotiations WHERE visitid > 359\")\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": ["visitid", "engagement"]}], "writes": [{"table": "labor_practices", "columns": ["visitid", "engagement"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"investment_rounds\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "investment_rounds", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT reservoir_name, siteid FROM tourdifferences LIMIT 31\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "tourdifferences", "columns": ["reservoir_name", "siteid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT yearid, drought_id FROM multimodal_trips\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"communityhealthworkers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "multimodal_trips", "columns": ["yearid", "drought_id"]}], "writes": [{"table": "communityhealthworkers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO fairtradecertifications SELECT 1\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO climate_finance SELECT chair_name, labor_hour_id FROM trade_history WHERE chair_name > 408\")\n", "labels": {"reads": [{"table": "trade_history", "columns": ["chair_name", "labor_hour_id"]}], "writes": [{"table": "climate_finance", "columns": ["chair_name", "labor_hour_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO mailshot_campaigns SELECT last_name, clubname FROM lives_in WHERE last_name > 120\"\n", "labels": {"reads": [{"table": "lives_in", "columns": ["last_name", "clubname"]}], "writes": [{"table": "mailshot_campaigns", "columns": ["last_name", "clubname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT attendee_id, source_system_code FROM doctors LIMIT 322\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "doctors", "columns": ["attendee_id", "source_system_code"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO smartcitytech (amount_claimed, violationid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "smartcitytech", "columns": ["amount_claimed", "violationid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT customer_first_name, eid FROM bi.bi_inventory\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"news_stories\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_inventory", "columns": ["customer_first_name", "eid"]}], "writes": [{"table": "news_stories", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO carbon_footprint SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO renewable_projects SELECT productcategory, union_name, song_release_year, how_to_get_there FROM menuitems WHERE productcategory > 46\")\n", "labels": {"reads": [{"table": "menuitems", "columns": ["productcategory", "union_name", "song_release_year", "how_to_get_there"]}], "writes": [{"table": "renewable_projects", "columns": ["productcategory", "union_name", "song_release_year", "how_to_get_there"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT installation_year, maintenance_contract_company_id FROM humanitarian_assistance LIMIT 413\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "humanitarian_assistance", "columns": ["installation_year", "maintenance_contract_company_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model forest_stats depends on school_details\ndbt build -s forest_stats --vars '{\"source_table\":\"school_details\"}'\n", "labels": {"reads": [{"table": "school_details", "columns": null}], "writes": [{"table": "forest_stats", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO immunization SELECT * FROM legacy\ncur.execute(\"SELECT zip, individual_last_name FROM ads.payments_di LIMIT 158\")\n", "labels": {"reads": [{"table": "ads.payments_di", "columns": ["zip", "individual_last_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 311;\nEOF\n", "labels": {"reads": [{"table": "emergencyservices", "columns": ["governor", "fabricid", "text"]}], "writes": [{"table": "tourism", "columns": ["governor", "fabricid", "text"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"benefits_overpayments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO project_timelines SELECT shipment_tracking_number, student FROM exhibition_visits WHERE shipment_tracking_number > 161\"], check=True)\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": ["shipment_tracking_number", "student"]}], "writes": [{"table": "project_timelines", "columns": ["shipment_tracking_number", "student"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO labor_practices (profits_in_billion, characteristic_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "labor_practices", "columns": ["profits_in_billion", "characteristic_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"education_union\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "education_union", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO carbon_offset_projects (asset_disposed_date, plan_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "carbon_offset_projects", "columns": ["asset_disposed_date", "plan_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.visit_details > 128).all()\n# src table: artists_valuation\nengine.execute(\"INSERT INTO skills SELECT * FROM artists_valuation\")\n", "labels": {"reads": [{"table": "artists_valuation", "columns": null}], "writes": [{"table": "skills", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO authenticationlogs SELECT shipping_mode, hotel_id FROM biotech.startups WHERE shipping_mode > 429\"\n", "labels": {"reads": [{"table": "biotech.startups", "columns": ["shipping_mode", "hotel_id"]}], "writes": [{"table": "authenticationlogs", "columns": ["shipping_mode", "hotel_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM vesselfuel\"\n", "labels": {"reads": [{"table": "vesselfuel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_input(ctx, \"education_programs\")\nupsert_to_target(df, \"reviews\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "education_programs", "columns": null}], "writes": [{"table": "reviews", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO threat_intelligence_budget SELECT menu_item_name, trainingtype FROM dwd.dwd_coupon_use_df WHERE menu_item_name > 216\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_coupon_use_df", "columns": ["menu_item_name", "trainingtype"]}], "writes": [{"table": "threat_intelligence_budget", "columns": ["menu_item_name", "trainingtype"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fault_log SELECT individual_last_name, build_date, reservoir_id FROM art_exhibit_attendance WHERE individual_last_name > 162\"], check=True)\n", "labels": {"reads": [{"table": "art_exhibit_attendance", "columns": ["individual_last_name", "build_date", "reservoir_id"]}], "writes": [{"table": "fault_log", "columns": ["individual_last_name", "build_date", "reservoir_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"unions\");\ndf.write().mode(\"overwrite\").saveAsTable(\"organic_products\");\n", "labels": {"reads": [{"table": "unions", "columns": null}], "writes": [{"table": "organic_products", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM climate_finance_organizations\", conn)\ndf.to_sql(\"waste_production\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}], "writes": [{"table": "waste_production", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO inspections SELECT manager_name, population, crs_description FROM public.collected_fare WHERE manager_name > 208\"\n", "labels": {"reads": [{"table": "public.collected_fare", "columns": ["manager_name", "population", "crs_description"]}], "writes": [{"table": "inspections", "columns": ["manager_name", "population", "crs_description"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dw.dw_risk_score_full SELECT siteid, temporary_acting FROM incident_region WHERE siteid > 400\"\n", "labels": {"reads": [{"table": "incident_region", "columns": ["siteid", "temporary_acting"]}], "writes": [{"table": "dw.dw_risk_score_full", "columns": ["siteid", "temporary_acting"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"claims_processing_stages\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "claims_processing_stages", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT train_id, sale_quantity FROM mentalhealthparityviolations LIMIT 249\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "mentalhealthparityviolations", "columns": ["train_id", "sale_quantity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"youth_fan_participation\");\ndf.write().mode(\"overwrite\").saveAsTable(\"products_booked\");\n", "labels": {"reads": [{"table": "youth_fan_participation", "columns": null}], "writes": [{"table": "products_booked", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_products_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mining_operations SELECT firm, coownerid FROM bioprocesses WHERE firm > 483\"], check=True)\n", "labels": {"reads": [{"table": "bioprocesses", "columns": ["firm", "coownerid"]}], "writes": [{"table": "mining_operations", "columns": ["firm", "coownerid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO stg.campaigns_daily SELECT feedback_id, price_range FROM staff_department_assignments WHERE feedback_id > 374\")\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["feedback_id", "price_range"]}], "writes": [{"table": "stg.campaigns_daily", "columns": ["feedback_id", "price_range"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO degrees SELECT interest_group, sustainability_score, team_name, attendee_id FROM hydro_power WHERE interest_group > 348\")\n", "labels": {"reads": [{"table": "hydro_power", "columns": ["interest_group", "sustainability_score", "team_name", "attendee_id"]}], "writes": [{"table": "degrees", "columns": ["interest_group", "sustainability_score", "team_name", "attendee_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM materials_usage\", conn)\ndf.to_sql(\"seafoodsouthafricakenya\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "materials_usage", "columns": null}], "writes": [{"table": "seafoodsouthafricakenya", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO irrigation_systems SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO canada_tech SELECT home_city, zip_postcode FROM train WHERE home_city > 94\")\n", "labels": {"reads": [{"table": "train", "columns": ["home_city", "zip_postcode"]}], "writes": [{"table": "canada_tech", "columns": ["home_city", "zip_postcode"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"city_properties\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "city_properties", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"auto_show\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"customers_cards\")\n", "labels": {"reads": [{"table": "auto_show", "columns": null}], "writes": [{"table": "customers_cards", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"flight_safety\");\ndf.write().mode(\"overwrite\").saveAsTable(\"company_info\");\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": [{"table": "company_info", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"packages\").toPandas()\ndf[[\"cust_id\", \"is_operational\"]].to_sql(\"disaster_zones\", engine, index=False)\n", "labels": {"reads": [{"table": "packages", "columns": null}], "writes": [{"table": "disaster_zones", "columns": ["cust_id", "is_operational"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"highest_scores\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "highest_scores", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO visits SELECT payment_date, problem_description, channel FROM erc20_transactions WHERE payment_date > 299\"], check=True)\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": ["payment_date", "problem_description", "channel"]}], "writes": [{"table": "visits", "columns": ["payment_date", "problem_description", "channel"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT asset_acquired_date, consultations FROM dws_events_df LIMIT 361\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "dws_events_df", "columns": ["asset_acquired_date", "consultations"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"marine_conservation\")\npersist_to_sink(df, \"ods_products_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "marine_conservation", "columns": null}], "writes": [{"table": "ods_products_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO ref_locations SELECT competition_type, offender_name, artist_gender FROM community_programs WHERE competition_type > 369\");\n", "labels": {"reads": [{"table": "community_programs", "columns": ["competition_type", "offender_name", "artist_gender"]}], "writes": [{"table": "ref_locations", "columns": ["competition_type", "offender_name", "artist_gender"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"arctic_sightings\")\nsink_to_sink(df, \"communityhealthworkers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "arctic_sightings", "columns": null}], "writes": [{"table": "communityhealthworkers", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM volume\"\n", "labels": {"reads": [{"table": "volume", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT singer_id, moisture FROM vessel_incident_count\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"policy_advocacy\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": ["singer_id", "moisture"]}], "writes": [{"table": "policy_advocacy", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO unesco_intangible_heritage SELECT drugname, organization, organisation_details, stay FROM spacecraft_manufacturers WHERE drugname > 106\");\n", "labels": {"reads": [{"table": "spacecraft_manufacturers", "columns": ["drugname", "organization", "organisation_details", "stay"]}], "writes": [{"table": "unesco_intangible_heritage", "columns": ["drugname", "organization", "organisation_details", "stay"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dwd.dwd_cart_item_di\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO carbon_prices SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"recovery_program\").where(\"dt = current_date()\").writeTo(\"weather\").append()\n", "labels": {"reads": [{"table": "recovery_program", "columns": null}], "writes": [{"table": "weather", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.payments_daily SELECT a.tot_cred, b.claim_type FROM fruitimport a JOIN stg.stg_exposure_daily b ON a.services = b.services\"\n", "labels": {"reads": [{"table": "fruitimport", "columns": null}, {"table": "stg.stg_exposure_daily", "columns": null}], "writes": [{"table": "bi.payments_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT inspection_time, evaluationid FROM faculty LIMIT 437\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO geothermal_power_plants SELECT call_id, departmentname FROM energy_prices WHERE call_id > 426\")\n", "labels": {"reads": [{"table": "faculty", "columns": ["inspection_time", "evaluationid"]}, {"table": "energy_prices", "columns": ["call_id", "departmentname"]}], "writes": [{"table": "geothermal_power_plants", "columns": ["call_id", "departmentname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 424;\nSQL\n", "labels": {"reads": [{"table": "shared_ebikes", "columns": ["participant_name", "char_cells"]}, {"table": "vr_tech", "columns": ["gender_mf", "location", "group_equity_shareholding"]}], "writes": [{"table": "street_markets", "columns": ["gender_mf", "location", "group_equity_shareholding"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws.dws_member_point_di SELECT * FROM legacy\ncur.execute(\"SELECT section_title, extractiondate FROM seasonalvegetables LIMIT 233\")\n", "labels": {"reads": [{"table": "seasonalvegetables", "columns": ["section_title", "extractiondate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mailshot_campaigns\").where(\"dt = current_date()\").writeTo(\"playerscores\").append()\n", "labels": {"reads": [{"table": "mailshot_campaigns", "columns": null}], "writes": [{"table": "playerscores", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"people\").toPandas()\ndf[[\"year_built\", \"model_year\"]].to_sql(\"space_missions_2\", engine, index=False)\n", "labels": {"reads": [{"table": "people", "columns": null}], "writes": [{"table": "space_missions_2", "columns": ["year_built", "model_year"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO inspection SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 248;\nEOF\n", "labels": {"reads": [{"table": "ocean_floor", "columns": ["number_of_observations", "phone_number", "spent", "category_id"]}], "writes": [{"table": "voyages", "columns": ["number_of_observations", "phone_number", "spent", "category_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"rnd_budget\");\ndf.write().mode(\"overwrite\").saveAsTable(\"fleets\");\n", "labels": {"reads": [{"table": "rnd_budget", "columns": null}], "writes": [{"table": "fleets", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"genetics.projects\")\ndump_to_sink(df, \"trainmaintenance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "genetics.projects", "columns": null}], "writes": [{"table": "trainmaintenance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO fishcaught SELECT drug_name, permitid, screen_mode FROM patient_satisfaction WHERE drug_name > 359\");\n", "labels": {"reads": [{"table": "patient_satisfaction", "columns": ["drug_name", "permitid", "screen_mode"]}], "writes": [{"table": "fishcaught", "columns": ["drug_name", "permitid", "screen_mode"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT forename, extraction_date FROM device_accessibility\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"manager\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "device_accessibility", "columns": ["forename", "extraction_date"]}], "writes": [{"table": "manager", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"musical\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"vessel_capacity\")\n", "labels": {"reads": [{"table": "musical", "columns": null}], "writes": [{"table": "vessel_capacity", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO venture (claim_id, special_features) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "venture", "columns": ["claim_id", "special_features"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nresult = value * ratio + offset\nsql = \"INSERT INTO armed_forces SELECT a.unsure_rate, b.health_equity_metric_2 FROM soilanalysis a JOIN atlantic_marine_life b ON a.aid_name = b.aid_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "soilanalysis", "columns": null}, {"table": "atlantic_marine_life", "columns": null}], "writes": [{"table": "armed_forces", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"fare_segments\").where(\"dt = current_date()\").writeTo(\"food_justice_contributors\").append()\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": [{"table": "food_justice_contributors", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM lessons\", conn)\ndf.to_sql(\"ods.ods_member_point_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "lessons", "columns": null}], "writes": [{"table": "ods.ods_member_point_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"india_solar_power\")\nsrc.write.insertInto(\"volunteers\", overwrite=True)\n", "labels": {"reads": [{"table": "india_solar_power", "columns": null}], "writes": [{"table": "volunteers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ytterbium_supply SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"vr_tech\")\nsrc.write.insertInto(\"extraction_methods\", overwrite=True)\n", "labels": {"reads": [{"table": "vr_tech", "columns": null}], "writes": [{"table": "extraction_methods", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO hotel_revenue SELECT shares, customer_id FROM co2price WHERE shares > 112\");\n", "labels": {"reads": [{"table": "co2price", "columns": ["shares", "customer_id"]}], "writes": [{"table": "hotel_revenue", "columns": ["shares", "customer_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO renewableprojects SELECT longitude, entrydate FROM virtual_tours_oceania WHERE longitude > 231\")\n", "labels": {"reads": [{"table": "virtual_tours_oceania", "columns": ["longitude", "entrydate"]}], "writes": [{"table": "renewableprojects", "columns": ["longitude", "entrydate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 228;\nEOF\n", "labels": {"reads": [{"table": "stg.stg_users_di", "columns": ["lawyer_name", "customername"]}], "writes": [{"table": "exhibition", "columns": ["lawyer_name", "customername"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO dwd.dwd_inventory_hourly SELECT line_1, characteristic_id, unit_id, game_id FROM healthcare_centers WHERE line_1 > 144\")\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": ["line_1", "characteristic_id", "unit_id", "game_id"]}], "writes": [{"table": "dwd.dwd_inventory_hourly", "columns": ["line_1", "characteristic_id", "unit_id", "game_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO vessel_registry SELECT a.disability, b.shelter_id FROM ods.shipments_df a JOIN stg.stg_users b ON a.region_code = b.region_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": null}, {"table": "stg.stg_users", "columns": null}], "writes": [{"table": "vessel_registry", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO game_sales SELECT update_date, area_name, classroom, satelliteid FROM course_authors_and_tutors WHERE update_date > 480\")\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": ["update_date", "area_name", "classroom", "satelliteid"]}], "writes": [{"table": "game_sales", "columns": ["update_date", "area_name", "classroom", "satelliteid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO attendance SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemicalbatches\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"battery_projects\")\n", "labels": {"reads": [{"table": "chemicalbatches", "columns": null}], "writes": [{"table": "battery_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemical\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "chemical", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bookings\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bookings", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM facility\", conn)\ndf.to_sql(\"disasters\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "facility", "columns": null}], "writes": [{"table": "disasters", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"casebilling\")\nsrc.write.insertInto(\"status\", overwrite=True)\n", "labels": {"reads": [{"table": "casebilling", "columns": null}], "writes": [{"table": "status", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO excavation_sites SELECT awardid, amount_donated, propertyid, accident_date FROM climate_communication WHERE awardid > 118\"\n", "labels": {"reads": [{"table": "climate_communication", "columns": ["awardid", "amount_donated", "propertyid", "accident_date"]}], "writes": [{"table": "excavation_sites", "columns": ["awardid", "amount_donated", "propertyid", "accident_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO al_jazeera_data (artist, reign) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "al_jazeera_data", "columns": ["artist", "reign"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model customer_transactions depends on sales_quarterly\ndbt build --models customer_transactions --vars '{\"source_table\":\"sales_quarterly\"}'\n", "labels": {"reads": [{"table": "sales_quarterly", "columns": null}], "writes": [{"table": "customer_transactions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO ref_product_categories SELECT treasurer_vote, donationid, flno FROM taxi_data WHERE treasurer_vote > 282\")\n", "labels": {"reads": [{"table": "taxi_data", "columns": ["treasurer_vote", "donationid", "flno"]}], "writes": [{"table": "ref_product_categories", "columns": ["treasurer_vote", "donationid", "flno"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM lenders\"\n", "labels": {"reads": [{"table": "lenders", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO voting_data SELECT winery, provider_parity_score FROM bikerental WHERE winery > 117\"\n", "labels": {"reads": [{"table": "bikerental", "columns": ["winery", "provider_parity_score"]}], "writes": [{"table": "voting_data", "columns": ["winery", "provider_parity_score"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT attack_id, category_id FROM fleets LIMIT 440\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "fleets", "columns": ["attack_id", "category_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ods.ods_risk_score_df\"\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO culturalcompetency SELECT grant_amount, firstdonationdate, platform_id, sustainable_practice FROM military_aircraft_maintenance WHERE grant_amount > 345\")\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": ["grant_amount", "firstdonationdate", "platform_id", "sustainable_practice"]}], "writes": [{"table": "culturalcompetency", "columns": ["grant_amount", "firstdonationdate", "platform_id", "sustainable_practice"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO basketball_match SELECT a.workshop_group_id, b.primary FROM files a JOIN opioid_overdoses b ON a.production_id = b.production_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "files", "columns": null}, {"table": "opioid_overdoses", "columns": null}], "writes": [{"table": "basketball_match", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO player_college (researcher, long) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "player_college", "columns": ["researcher", "long"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO art (shop_details, asset_disposed_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "art", "columns": ["shop_details", "asset_disposed_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO inventory (min_salary, updated_at) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "inventory", "columns": ["min_salary", "updated_at"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO projectemployees SELECT isfirstattendee, stock, emission_date FROM mediterranean_salinity WHERE isfirstattendee > 370\"\n", "labels": {"reads": [{"table": "mediterranean_salinity", "columns": ["isfirstattendee", "stock", "emission_date"]}], "writes": [{"table": "projectemployees", "columns": ["isfirstattendee", "stock", "emission_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 155;\nEOF\n", "labels": {"reads": [{"table": "biotech.startups", "columns": ["contactid", "society"]}], "writes": [{"table": "undergoes", "columns": ["contactid", "society"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO accelerator_compatible_browser SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO fish_purchases SELECT is_operational, jul, union_member_id FROM sustainable_warehouses WHERE is_operational > 148\");\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": ["is_operational", "jul", "union_member_id"]}], "writes": [{"table": "fish_purchases", "columns": ["is_operational", "jul", "union_member_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO train SELECT num_of_component, art_type, asset_disposed_date FROM teacher_professional_development WHERE num_of_component > 402\"\n", "labels": {"reads": [{"table": "teacher_professional_development", "columns": ["num_of_component", "art_type", "asset_disposed_date"]}], "writes": [{"table": "train", "columns": ["num_of_component", "art_type", "asset_disposed_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"investments_esg\").where(\"dt = current_date()\").writeTo(\"complaints_breakdown\").append()\n", "labels": {"reads": [{"table": "investments_esg", "columns": null}], "writes": [{"table": "complaints_breakdown", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dwd.dwd_payments_di SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO permit SELECT transit_passengers, partitionid, orderid, last_service FROM mentalhealthproviders WHERE transit_passengers > 186\"\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": ["transit_passengers", "partitionid", "orderid", "last_service"]}], "writes": [{"table": "permit", "columns": ["transit_passengers", "partitionid", "orderid", "last_service"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ais\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"neighborhoods\")\n", "labels": {"reads": [{"table": "ais", "columns": null}], "writes": [{"table": "neighborhoods", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO financial_capability_programs SELECT bicycle_id, individual_middle_name FROM causes WHERE bicycle_id > 401\");\n", "labels": {"reads": [{"table": "causes", "columns": ["bicycle_id", "individual_middle_name"]}], "writes": [{"table": "financial_capability_programs", "columns": ["bicycle_id", "individual_middle_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart_orders_di SELECT * FROM legacy\ncur.execute(\"SELECT flight_number, funding_source FROM laptimes LIMIT 15\")\n", "labels": {"reads": [{"table": "laptimes", "columns": ["flight_number", "funding_source"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO imagery_archive SELECT sustainability_rating, event_name, business_id FROM decentralized_apps WHERE sustainability_rating > 178\");\n", "labels": {"reads": [{"table": "decentralized_apps", "columns": ["sustainability_rating", "event_name", "business_id"]}], "writes": [{"table": "imagery_archive", "columns": ["sustainability_rating", "event_name", "business_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO mentalhealthscores SELECT num_of_stock, projectid FROM global_sales_2022 WHERE num_of_stock > 129\");\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": ["num_of_stock", "projectid"]}], "writes": [{"table": "mentalhealthscores", "columns": ["num_of_stock", "projectid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO product_catalog SELECT founding_year, customer_type_code FROM station_emergencies WHERE founding_year > 55\")\n", "labels": {"reads": [{"table": "station_emergencies", "columns": ["founding_year", "customer_type_code"]}], "writes": [{"table": "product_catalog", "columns": ["founding_year", "customer_type_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT offset_id, co2_emission FROM tracks LIMIT 164\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO stg.stg_shipments_hourly SELECT enr, spill_name, course_completion, application_date FROM ocean WHERE enr > 346\")\n", "labels": {"reads": [{"table": "tracks", "columns": ["offset_id", "co2_emission"]}, {"table": "ocean", "columns": ["enr", "spill_name", "course_completion", "application_date"]}], "writes": [{"table": "stg.stg_shipments_hourly", "columns": ["enr", "spill_name", "course_completion", "application_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT num_of_stock, station_name FROM academic_publications LIMIT 287\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "academic_publications", "columns": ["num_of_stock", "station_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"pilot\").toPandas()\ndf[[\"generation_date\", \"cruelty_free\"]].to_sql(\"pollutionincidents\", engine, index=False)\n", "labels": {"reads": [{"table": "pilot", "columns": null}], "writes": [{"table": "pollutionincidents", "columns": ["generation_date", "cruelty_free"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mine SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO consumer SELECT min_dew_point_f, programarea FROM market WHERE min_dew_point_f > 487\")\n", "labels": {"reads": [{"table": "market", "columns": ["min_dew_point_f", "programarea"]}], "writes": [{"table": "consumer", "columns": ["min_dew_point_f", "programarea"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"restaurant_revenue\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artist_data\")\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": null}], "writes": [{"table": "artist_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO stg.stg_clicks_delta SELECT * FROM legacy\nspark.sql(\"INSERT INTO dwd.dwd_payments SELECT attendance, oct, health_equity_metric_3, time_month FROM volunteer_hours WHERE attendance > 341\")\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": ["attendance", "oct", "health_equity_metric_3", "time_month"]}], "writes": [{"table": "dwd.dwd_payments", "columns": ["attendance", "oct", "health_equity_metric_3", "time_month"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"product_review\")\nwrite_to_store(df, \"community.donors\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "product_review", "columns": null}], "writes": [{"table": "community.donors", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sustainable_projects SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sales_count > 480).all()\n# src table: ads.ads_device_log_di\nengine.execute(\"INSERT INTO defense_diplomacy SELECT * FROM ads.ads_device_log_di\")\n", "labels": {"reads": [{"table": "ads.ads_device_log_di", "columns": null}], "writes": [{"table": "defense_diplomacy", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO carbon_offset_projects SELECT heart_rate, totalamount FROM military_sales WHERE heart_rate > 419\")\n", "labels": {"reads": [{"table": "military_sales", "columns": ["heart_rate", "totalamount"]}], "writes": [{"table": "carbon_offset_projects", "columns": ["heart_rate", "totalamount"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"exit_strategy\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "exit_strategy", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT billingamount, product_category FROM purchase LIMIT 441\")\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO crime_stats SELECT drug_id, guest_first_name, innovation_id, vehicletype FROM eco_diversification_investment WHERE drug_id > 340\")\n", "labels": {"reads": [{"table": "purchase", "columns": ["billingamount", "product_category"]}, {"table": "eco_diversification_investment", "columns": ["drug_id", "guest_first_name", "innovation_id", "vehicletype"]}], "writes": [{"table": "crime_stats", "columns": ["drug_id", "guest_first_name", "innovation_id", "vehicletype"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bike_stations SELECT vaccinations, founders_lgbtq, team_id_br, restaurant FROM human_resources WHERE vaccinations > 342\"\n", "labels": {"reads": [{"table": "human_resources", "columns": ["vaccinations", "founders_lgbtq", "team_id_br", "restaurant"]}], "writes": [{"table": "bike_stations", "columns": ["vaccinations", "founders_lgbtq", "team_id_br", "restaurant"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO traffic SELECT is_organic, part_id, lname FROM shipment WHERE is_organic > 333\"], check=True)\n", "labels": {"reads": [{"table": "shipment", "columns": ["is_organic", "part_id", "lname"]}], "writes": [{"table": "traffic", "columns": ["is_organic", "part_id", "lname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"travel_advisory\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"route_fares\")\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": [{"table": "route_fares", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO rebounds SELECT production_usage, num_of_audience, installation_year FROM humanitarian_aid WHERE production_usage > 491\"\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": ["production_usage", "num_of_audience", "installation_year"]}], "writes": [{"table": "rebounds", "columns": ["production_usage", "num_of_audience", "installation_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dp_articles SELECT * FROM legacy\nspark.sql(\"INSERT INTO student_addresses SELECT rental_date, jobcategory, fuelconsumed FROM caribbeansea WHERE rental_date > 186\")\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["rental_date", "jobcategory", "fuelconsumed"]}], "writes": [{"table": "student_addresses", "columns": ["rental_date", "jobcategory", "fuelconsumed"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 55;\nEOF\n", "labels": {"reads": [{"table": "player", "columns": ["fan_name", "taxi_id", "event_name", "artifactname"]}], "writes": [{"table": "sustainableprojects", "columns": ["fan_name", "taxi_id", "event_name", "artifactname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"digital_trends\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "digital_trends", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table artcontributors --columns workout_date,update_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "artcontributors", "columns": ["workout_date", "update_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM communitycenters\", conn)\ndf.to_sql(\"tourismproviders\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "communitycenters", "columns": null}], "writes": [{"table": "tourismproviders", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"astronaut_missions\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"low_value_contracts\")\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": null}], "writes": [{"table": "low_value_contracts", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO driverstandings SELECT * FROM legacy\ncur.execute(\"SELECT incident_type_description, arrival FROM military_tech LIMIT 432\")\n", "labels": {"reads": [{"table": "military_tech", "columns": ["incident_type_description", "arrival"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"wastedata\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "wastedata", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model agro_regions depends on agencies\ndbt run -s agro_regions --vars '{\"source_table\":\"agencies\"}'\n", "labels": {"reads": [{"table": "agencies", "columns": null}], "writes": [{"table": "agro_regions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO program_budget SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO songs SELECT categoryid, membergender, business_size FROM patient WHERE categoryid > 355\");\n", "labels": {"reads": [{"table": "patient", "columns": ["categoryid", "membergender", "business_size"]}], "writes": [{"table": "songs", "columns": ["categoryid", "membergender", "business_size"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"genetics.crispr\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"audience\")\n", "labels": {"reads": [{"table": "genetics.crispr", "columns": null}], "writes": [{"table": "audience", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO unions SELECT * FROM legacy\nspark.sql(\"INSERT INTO region_stats SELECT orderdate, party_name, coalid FROM lenders WHERE orderdate > 157\")\n", "labels": {"reads": [{"table": "lenders", "columns": ["orderdate", "party_name", "coalid"]}], "writes": [{"table": "region_stats", "columns": ["orderdate", "party_name", "coalid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bike_share\");\ndf.write().mode(\"overwrite\").saveAsTable(\"innovation_projects\");\n", "labels": {"reads": [{"table": "bike_share", "columns": null}], "writes": [{"table": "innovation_projects", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT drill_count, preference_score FROM dw.dw_users_di\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"ads_users_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": ["drill_count", "preference_score"]}], "writes": [{"table": "ads_users_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ods.ods_coupon_use_delta\"\n", "labels": {"reads": [{"table": "ods.ods_coupon_use_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods.ods_exposure_delta SELECT policy_count, gamepreference, negative FROM europium_exports WHERE policy_count > 72\"\n", "labels": {"reads": [{"table": "europium_exports", "columns": ["policy_count", "gamepreference", "negative"]}], "writes": [{"table": "ods.ods_exposure_delta", "columns": ["policy_count", "gamepreference", "negative"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table organization --target-dir /tmp/land\n", "labels": {"reads": [{"table": "organization", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mentalhealthprovider (price_in_dollar, sale_volume) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mentalhealthprovider", "columns": ["price_in_dollar", "sale_volume"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO researchprojects SELECT ai_powered_features, location_description, state_name FROM accommodations WHERE ai_powered_features > 200\"\n", "labels": {"reads": [{"table": "accommodations", "columns": ["ai_powered_features", "location_description", "state_name"]}], "writes": [{"table": "researchprojects", "columns": ["ai_powered_features", "location_description", "state_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stores_2 SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO carbon_offsets.carbon_offsets SELECT is_valid, budget_allocation, ethical_manufacturing FROM reservations WHERE is_valid > 66\")\n", "labels": {"reads": [{"table": "reservations", "columns": ["is_valid", "budget_allocation", "ethical_manufacturing"]}], "writes": [{"table": "carbon_offsets.carbon_offsets", "columns": ["is_valid", "budget_allocation", "ethical_manufacturing"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.stg_users_full SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO sites SELECT floors, review_text, meter_300 FROM product_revenue WHERE floors > 329\")\n", "labels": {"reads": [{"table": "product_revenue", "columns": ["floors", "review_text", "meter_300"]}], "writes": [{"table": "sites", "columns": ["floors", "review_text", "meter_300"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mart_shipments_full\");\ndf.write().mode(\"overwrite\").saveAsTable(\"platform_production\");\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": null}], "writes": [{"table": "platform_production", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws.dws_refunds_hourly\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"teachers\")\n", "labels": {"reads": [{"table": "dws.dws_refunds_hourly", "columns": null}], "writes": [{"table": "teachers", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM crop_temperature\", conn)\ndf.to_sql(\"insurancetype\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "crop_temperature", "columns": null}], "writes": [{"table": "insurancetype", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ads_vendors_hourly\").where(\"dt = current_date()\").writeTo(\"conservation_projects\").append()\n", "labels": {"reads": [{"table": "ads_vendors_hourly", "columns": null}], "writes": [{"table": "conservation_projects", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM tb_cases\"\n", "labels": {"reads": [{"table": "tb_cases", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO customer_transactions SELECT attraction_name, ai_model, headquarters FROM block WHERE attraction_name > 457\")\n", "labels": {"reads": [{"table": "block", "columns": ["attraction_name", "ai_model", "headquarters"]}], "writes": [{"table": "customer_transactions", "columns": ["attraction_name", "ai_model", "headquarters"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"resource_extraction\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "resource_extraction", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"concert_revenue\").where(\"dt = current_date()\").writeTo(\"public.trips_by_day_train\").append()\n", "labels": {"reads": [{"table": "concert_revenue", "columns": null}], "writes": [{"table": "public.trips_by_day_train", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"carbon_footprint\");\ndf.write().mode(\"overwrite\").saveAsTable(\"reo_production\");\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": null}], "writes": [{"table": "reo_production", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO apartment_bookings SELECT a.court_id, b.number_of_sightings FROM province.human_rights_data a JOIN laborstatistics b ON a.pid = b.pid\"\n", "labels": {"reads": [{"table": "province.human_rights_data", "columns": null}, {"table": "laborstatistics", "columns": null}], "writes": [{"table": "apartment_bookings", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table eco_diversification_investment --columns countryname,business_zone --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "eco_diversification_investment", "columns": ["countryname", "business_zone"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO purchases (star_rating_code, port) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "purchases", "columns": ["star_rating_code", "port"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fireincidents\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "fireincidents", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nimport logging\nsql = \"INSERT INTO user_video_view SELECT a.primary_advisor, b.student_capacity FROM community_programs a JOIN dws.dws_cart_item_daily b ON a.date_claim_settled = b.date_claim_settled\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "community_programs", "columns": null}, {"table": "dws.dws_cart_item_daily", "columns": null}], "writes": [{"table": "user_video_view", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO match_result SELECT delivery_time, class, volunteer_quarter, discovery_date FROM album WHERE delivery_time > 358\"\n", "labels": {"reads": [{"table": "album", "columns": ["delivery_time", "class", "volunteer_quarter", "discovery_date"]}], "writes": [{"table": "match_result", "columns": ["delivery_time", "class", "volunteer_quarter", "discovery_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO visual_arts SELECT warehouseid, city, agegroup FROM communitycenters WHERE warehouseid > 22\")\n", "labels": {"reads": [{"table": "communitycenters", "columns": ["warehouseid", "city", "agegroup"]}], "writes": [{"table": "visual_arts", "columns": ["warehouseid", "city", "agegroup"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO channel SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart.mart_device_log\"\n", "labels": {"reads": [{"table": "mart.mart_device_log", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO farmers_india SELECT donorid, category_id, outcome_type FROM game_scores WHERE donorid > 366\"\n", "labels": {"reads": [{"table": "game_scores", "columns": ["donorid", "category_id", "outcome_type"]}], "writes": [{"table": "farmers_india", "columns": ["donorid", "category_id", "outcome_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO waste SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO carbon_prices_3 SELECT sales_details, budget_allocated FROM bridges WHERE sales_details > 296\"], check=True)\n", "labels": {"reads": [{"table": "bridges", "columns": ["sales_details", "budget_allocated"]}], "writes": [{"table": "carbon_prices_3", "columns": ["sales_details", "budget_allocated"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"film\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "film", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT rank_in_round, hire_date FROM seamounts LIMIT 341\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO electric_buses SELECT depth, crossing, wellname FROM agri_innov WHERE depth > 378\")\n", "labels": {"reads": [{"table": "seamounts", "columns": ["rank_in_round", "hire_date"]}, {"table": "agri_innov", "columns": ["depth", "crossing", "wellname"]}], "writes": [{"table": "electric_buses", "columns": ["depth", "crossing", "wellname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table diseases --columns productionrate,total_distance --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "diseases", "columns": ["productionrate", "total_distance"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"customers_cards\")\nsrc.write.insertInto(\"solana_transactions\", overwrite=True)\n", "labels": {"reads": [{"table": "customers_cards", "columns": null}], "writes": [{"table": "solana_transactions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO healthequitymetrics SELECT black, ironquantity FROM phone_market WHERE black > 436\")\n", "labels": {"reads": [{"table": "phone_market", "columns": ["black", "ironquantity"]}], "writes": [{"table": "healthequitymetrics", "columns": ["black", "ironquantity"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"warehouses\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ods.ods_events_daily\")\n", "labels": {"reads": [{"table": "warehouses", "columns": null}], "writes": [{"table": "ods.ods_events_daily", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO coowners SELECT number_of_vessels, building_full_name, fanid FROM stg.campaigns_df WHERE number_of_vessels > 221\"\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": ["number_of_vessels", "building_full_name", "fanid"]}], "writes": [{"table": "coowners", "columns": ["number_of_vessels", "building_full_name", "fanid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart.mart_coupon_use_full --columns duration,asset_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_coupon_use_full", "columns": ["duration", "asset_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"exhibition_record\");\ndf.write().mode(\"overwrite\").saveAsTable(\"materials\");\n", "labels": {"reads": [{"table": "exhibition_record", "columns": null}], "writes": [{"table": "materials", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"programs\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "programs", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO water_consumption SELECT a.wheels, b.truck_details FROM experts a JOIN mart.mart_products_hourly b ON a.dates_active = b.dates_active\"\n", "labels": {"reads": [{"table": "experts", "columns": null}, {"table": "mart.mart_products_hourly", "columns": null}], "writes": [{"table": "water_consumption", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO stg.refunds SELECT passenger_count, container_id, security_level, plantlocation FROM courtcases WHERE passenger_count > 194\");\n", "labels": {"reads": [{"table": "courtcases", "columns": ["passenger_count", "container_id", "security_level", "plantlocation"]}], "writes": [{"table": "stg.refunds", "columns": ["passenger_count", "container_id", "security_level", "plantlocation"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM pollution_initiatives\"\n", "labels": {"reads": [{"table": "pollution_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ods.clicks_full SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT fault_short_name, drug_name FROM teacher_development_race\", engine)\nif not rows:\n logger.warning('empty result')\nimport logging\ndf.to_sql(\"lots\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": ["fault_short_name", "drug_name"]}], "writes": [{"table": "lots", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO document_types SELECT carriername, cultivatorid, sessiondate FROM cinema WHERE carriername > 351\"\n", "labels": {"reads": [{"table": "cinema", "columns": ["carriername", "cultivatorid", "sessiondate"]}], "writes": [{"table": "document_types", "columns": ["carriername", "cultivatorid", "sessiondate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO park SELECT 1\"\nlogger.info(msg)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 449;\nEOF\n", "labels": {"reads": [{"table": "roads", "columns": ["crime_id", "scan_date", "manufacturerid"]}], "writes": [{"table": "resilience_infrastructure", "columns": ["crime_id", "scan_date", "manufacturerid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO player_college SELECT a.tourist_attraction_id, b.date_order_placed FROM venture a JOIN dwd_events_delta b ON a.num_cases = b.num_cases\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "venture", "columns": null}, {"table": "dwd_events_delta", "columns": null}], "writes": [{"table": "player_college", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO demographics SELECT species_name, habitat_id FROM communitydevelopment WHERE species_name > 25\")\n", "labels": {"reads": [{"table": "communitydevelopment", "columns": ["species_name", "habitat_id"]}], "writes": [{"table": "demographics", "columns": ["species_name", "habitat_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.campaigns_full\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.campaigns_full", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"materials_usage\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "materials_usage", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bike_share\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.shipments_full\")\n", "labels": {"reads": [{"table": "bike_share", "columns": null}], "writes": [{"table": "mart.shipments_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO art SELECT haslegalprecedent, goal_id FROM therapy_attendance WHERE haslegalprecedent > 265\");\n", "labels": {"reads": [{"table": "therapy_attendance", "columns": ["haslegalprecedent", "goal_id"]}], "writes": [{"table": "art", "columns": ["haslegalprecedent", "goal_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO industry_funding SELECT years_operating, spending, program_category FROM fashion_trend_data WHERE years_operating > 36\")\n", "labels": {"reads": [{"table": "fashion_trend_data", "columns": ["years_operating", "spending", "program_category"]}], "writes": [{"table": "industry_funding", "columns": ["years_operating", "spending", "program_category"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO social_issues SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model gamedesigndata depends on digital_assets\ndbt run -s gamedesigndata --vars 'source: digital_assets'\n", "labels": {"reads": [{"table": "digital_assets", "columns": null}], "writes": [{"table": "gamedesigndata", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO spacecraft SELECT agreementid, injury, has_aloe_vera, materialtype FROM defense_projects_sales WHERE agreementid > 221\");\n", "labels": {"reads": [{"table": "defense_projects_sales", "columns": ["agreementid", "injury", "has_aloe_vera", "materialtype"]}], "writes": [{"table": "spacecraft", "columns": ["agreementid", "injury", "has_aloe_vera", "materialtype"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO album SELECT monthly_rental, reported FROM beverages WHERE monthly_rental > 269\"\n", "labels": {"reads": [{"table": "beverages", "columns": ["monthly_rental", "reported"]}], "writes": [{"table": "album", "columns": ["monthly_rental", "reported"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT working_year_starts, decision FROM royal_family LIMIT 421\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "royal_family", "columns": ["working_year_starts", "decision"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO customer SELECT is_organic, commodity, totalamount FROM contracts WHERE is_organic > 458\"\n", "labels": {"reads": [{"table": "contracts", "columns": ["is_organic", "commodity", "totalamount"]}], "writes": [{"table": "customer", "columns": ["is_organic", "commodity", "totalamount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO tourdifferences SELECT a.crop_name, b.itemname FROM membership_register_branch a JOIN hotel_ratings b ON a.household_size = b.household_size\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "membership_register_branch", "columns": null}, {"table": "hotel_ratings", "columns": null}], "writes": [{"table": "tourdifferences", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO biodiversity (amount_piad, customer_details) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "biodiversity", "columns": ["amount_piad", "customer_details"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model safety_incidents_india depends on thefttypes\ndbt build --models safety_incidents_india --vars 'source: thefttypes'\n", "labels": {"reads": [{"table": "thefttypes", "columns": null}], "writes": [{"table": "safety_incidents_india", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO apartments (num_solo_exhibitions, reo_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "apartments", "columns": ["num_solo_exhibitions", "reo_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model menu_engineering depends on publication\ndbt build -s menu_engineering --vars 'source: publication'\n", "labels": {"reads": [{"table": "publication", "columns": null}], "writes": [{"table": "menu_engineering", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sector_incidents SELECT a.contributorid, b.policy_id FROM taj_mahal_visitors a JOIN pipelines_us_canada b ON a.participant_type_code = b.participant_type_code\"\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": null}, {"table": "pipelines_us_canada", "columns": null}], "writes": [{"table": "sector_incidents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table player_sessions --columns src_apid,cmi_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "player_sessions", "columns": ["src_apid", "cmi_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"assets_frameworks\").toPandas()\ndf[[\"average\", \"menuitemid\"]].to_sql(\"carbon_prices_3\", engine, index=False)\n", "labels": {"reads": [{"table": "assets_frameworks", "columns": null}], "writes": [{"table": "carbon_prices_3", "columns": ["average", "menuitemid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO trade_history SELECT hours, comment_count FROM landfill_capacity_city_v2 WHERE hours > 410\"], check=True)\n", "labels": {"reads": [{"table": "landfill_capacity_city_v2", "columns": ["hours", "comment_count"]}], "writes": [{"table": "trade_history", "columns": ["hours", "comment_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT genreid, organic_matter FROM hotels\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"mart.mart_coupon_use_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hotels", "columns": ["genreid", "organic_matter"]}], "writes": [{"table": "mart.mart_coupon_use_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table rural_economy_2 --columns ai_algorithm_id,played --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "rural_economy_2", "columns": ["ai_algorithm_id", "played"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.apt_number > 307).all()\n# src table: public.forest_stats\nengine.execute(\"INSERT INTO county_public_safety SELECT * FROM public.forest_stats\")\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": null}], "writes": [{"table": "county_public_safety", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_input(ctx, \"wildlife\")\nupsert_to_warehouse(df, \"greenbuildings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "wildlife", "columns": null}], "writes": [{"table": "greenbuildings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO program_history (restaurant_name, museumid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "program_history", "columns": ["restaurant_name", "museumid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO renewable_projects (facilityid, artwork_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "renewable_projects", "columns": ["facilityid", "artwork_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"satellites_by_country\").where(\"dt = current_date()\").writeTo(\"industrial_customers\").append()\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": null}], "writes": [{"table": "industrial_customers", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO jupiter_missions (lot_details, vendor) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "jupiter_missions", "columns": ["lot_details", "vendor"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"community_health_center\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"station_crime_rates\")\n", "labels": {"reads": [{"table": "community_health_center", "columns": null}], "writes": [{"table": "station_crime_rates", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dispensary_id, review_rating FROM rental LIMIT 151\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "rental", "columns": ["dispensary_id", "review_rating"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mentalhealthprofessional\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ocean_health_monitor\");\n", "labels": {"reads": [{"table": "mentalhealthprofessional", "columns": null}], "writes": [{"table": "ocean_health_monitor", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO genetics.projects SELECT * FROM legacy\ncur.execute(\"SELECT platform_id, inspection_id FROM mart.shipments_full LIMIT 216\")\n", "labels": {"reads": [{"table": "mart.shipments_full", "columns": ["platform_id", "inspection_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dws.dws_inventory_di SELECT publication_date, loan_type, aid, vendor_id FROM electric_taxis WHERE publication_date > 208\"\n", "labels": {"reads": [{"table": "electric_taxis", "columns": ["publication_date", "loan_type", "aid", "vendor_id"]}], "writes": [{"table": "dws.dws_inventory_di", "columns": ["publication_date", "loan_type", "aid", "vendor_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"energy_consumption\")\nsrc.write.insertInto(\"wastewatertreatment\", overwrite=True)\n", "labels": {"reads": [{"table": "energy_consumption", "columns": null}], "writes": [{"table": "wastewatertreatment", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT animal, ethnicity FROM programoutcomes\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"ai_papers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "programoutcomes", "columns": ["animal", "ethnicity"]}], "writes": [{"table": "ai_papers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT lender_id, sustainabilityid FROM mine LIMIT 449\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO party_events SELECT maintenance_date, sea FROM donations2022 WHERE maintenance_date > 479\")\n", "labels": {"reads": [{"table": "mine", "columns": ["lender_id", "sustainabilityid"]}, {"table": "donations2022", "columns": ["maintenance_date", "sea"]}], "writes": [{"table": "party_events", "columns": ["maintenance_date", "sea"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"thefttypes\").toPandas()\ndf[[\"club_name\", \"grant_start_date\"]].to_sql(\"hospital_visits\", engine, index=False)\n", "labels": {"reads": [{"table": "thefttypes", "columns": null}], "writes": [{"table": "hospital_visits", "columns": ["club_name", "grant_start_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO open_pedagogy_courses (aircraft_id, environmental_impact) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "open_pedagogy_courses", "columns": ["aircraft_id", "environmental_impact"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthcare_centers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"underwater_cables\")\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": null}], "writes": [{"table": "underwater_cables", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"agroecology_practices\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart_refunds_delta\")\n", "labels": {"reads": [{"table": "agroecology_practices", "columns": null}], "writes": [{"table": "mart_refunds_delta", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_input(ctx, \"accessible_tech_categories\")\nexport_to_output(df, \"street_markets\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": null}], "writes": [{"table": "street_markets", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"co2_emissions\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "co2_emissions", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO airport_aircraft (storename, apt_number) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "airport_aircraft", "columns": ["storename", "apt_number"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO state_contracts SELECT asset_id, stars FROM digital_divide_initiatives WHERE asset_id > 73\"\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": ["asset_id", "stars"]}], "writes": [{"table": "state_contracts", "columns": ["asset_id", "stars"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO nutrition_facts SELECT energy_source, num_rooms FROM dws_coupon_use WHERE energy_source > 277\");\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["energy_source", "num_rooms"]}], "writes": [{"table": "nutrition_facts", "columns": ["energy_source", "num_rooms"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM steps\", conn)\ndf.to_sql(\"trips\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "steps", "columns": null}], "writes": [{"table": "trips", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO socially_responsible_loans SELECT projectname, yield_id, watertemp, invoice_number FROM aircraftsquadrons WHERE projectname > 426\"\n", "labels": {"reads": [{"table": "aircraftsquadrons", "columns": ["projectname", "yield_id", "watertemp", "invoice_number"]}], "writes": [{"table": "socially_responsible_loans", "columns": ["projectname", "yield_id", "watertemp", "invoice_number"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO landfill_capacity SELECT job_id, offset_id, fabrictype, payment_method FROM euroavev WHERE job_id > 135\"\n", "labels": {"reads": [{"table": "euroavev", "columns": ["job_id", "offset_id", "fabrictype", "payment_method"]}], "writes": [{"table": "landfill_capacity", "columns": ["job_id", "offset_id", "fabrictype", "payment_method"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"space_telescopes\").toPandas()\ndf[[\"job_id\", \"cost\"]].to_sql(\"sales_quarterly\", engine, index=False)\n", "labels": {"reads": [{"table": "space_telescopes", "columns": null}], "writes": [{"table": "sales_quarterly", "columns": ["job_id", "cost"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO apartment_buildings SELECT purchaseid, nation, journal_id FROM artist_demographics WHERE purchaseid > 39\");\n", "labels": {"reads": [{"table": "artist_demographics", "columns": ["purchaseid", "nation", "journal_id"]}], "writes": [{"table": "apartment_buildings", "columns": ["purchaseid", "nation", "journal_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.scientist > 434).all()\n# src table: skincareproducts\nengine.execute(\"INSERT INTO digitalliteracytraining SELECT * FROM skincareproducts\")\n", "labels": {"reads": [{"table": "skincareproducts", "columns": null}], "writes": [{"table": "digitalliteracytraining", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO staff SELECT claim_id, time_day, market_details, sales_details FROM building_permits WHERE claim_id > 158\"\n", "labels": {"reads": [{"table": "building_permits", "columns": ["claim_id", "time_day", "market_details", "sales_details"]}], "writes": [{"table": "staff", "columns": ["claim_id", "time_day", "market_details", "sales_details"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO livestock SELECT news_story_id, outcome_description FROM asset_parts WHERE news_story_id > 114\");\n", "labels": {"reads": [{"table": "asset_parts", "columns": ["news_story_id", "outcome_description"]}], "writes": [{"table": "livestock", "columns": ["news_story_id", "outcome_description"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO season_assists SELECT days, date_of_completion, booking_date FROM ref_service_types WHERE days > 2\"], check=True)\n", "labels": {"reads": [{"table": "ref_service_types", "columns": ["days", "date_of_completion", "booking_date"]}], "writes": [{"table": "season_assists", "columns": ["days", "date_of_completion", "booking_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO shariah_compliant_loans SELECT reporter_id, creation_year FROM ods.sessions WHERE reporter_id > 71\")\n", "labels": {"reads": [{"table": "ods.sessions", "columns": ["reporter_id", "creation_year"]}], "writes": [{"table": "shariah_compliant_loans", "columns": ["reporter_id", "creation_year"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"sustainable_fabrics\")\nsrc.write.insertInto(\"environmental_impact_stats\", overwrite=True)\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": null}], "writes": [{"table": "environmental_impact_stats", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"policyimpact\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cyber_incidents\")\n", "labels": {"reads": [{"table": "policyimpact", "columns": null}], "writes": [{"table": "cyber_incidents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO experts SELECT a.classroom, b.patient_count FROM dws.dws_refunds_daily a JOIN mart.mart_refunds_di b ON a.funding_source = b.funding_source\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_daily", "columns": null}, {"table": "mart.mart_refunds_di", "columns": null}], "writes": [{"table": "experts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO teacher_pd SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"climate_adaptation_projects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "climate_adaptation_projects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"projecttimeline\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "projecttimeline", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM advisor\"\n", "labels": {"reads": [{"table": "advisor", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO store_district SELECT * FROM legacy\ncur.execute(\"SELECT workout_duration, user_account FROM exhibitionattendance LIMIT 90\")\n", "labels": {"reads": [{"table": "exhibitionattendance", "columns": ["workout_duration", "user_account"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"renewable_projects\").where(\"dt = current_date()\").writeTo(\"recycling_centers\").append()\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}], "writes": [{"table": "recycling_centers", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO school_enrollment SELECT total_employees, menu_item_name, promotiondate, algorithm FROM exhibition WHERE total_employees > 63\"], check=True)\n", "labels": {"reads": [{"table": "exhibition", "columns": ["total_employees", "menu_item_name", "promotiondate", "algorithm"]}], "writes": [{"table": "school_enrollment", "columns": ["total_employees", "menu_item_name", "promotiondate", "algorithm"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO restorative_justice_sentences SELECT * FROM legacy\ncur.execute(\"SELECT round_date, user_id FROM stg.refunds LIMIT 293\")\n", "labels": {"reads": [{"table": "stg.refunds", "columns": ["round_date", "user_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT deliverydate, vaccination_status FROM product_ingredient LIMIT 378\")\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO veteran_employment SELECT contract_end, veteran_unemployment_rate, heritage_site FROM individual WHERE contract_end > 252\")\n", "labels": {"reads": [{"table": "product_ingredient", "columns": ["deliverydate", "vaccination_status"]}, {"table": "individual", "columns": ["contract_end", "veteran_unemployment_rate", "heritage_site"]}], "writes": [{"table": "veteran_employment", "columns": ["contract_end", "veteran_unemployment_rate", "heritage_site"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO arcticocean SELECT participant, centername, role, handling_id FROM humanitarian_aid WHERE participant > 188\"], check=True)\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": ["participant", "centername", "role", "handling_id"]}], "writes": [{"table": "arcticocean", "columns": ["participant", "centername", "role", "handling_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO circular_economy (i_id, events) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "circular_economy", "columns": ["i_id", "events"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.dws_clicks_full (city_population, rid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_clicks_full", "columns": ["city_population", "rid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO rent_arrears SELECT * FROM legacy\nspark.sql(\"INSERT INTO fashion_trend_data SELECT stat_id, investment_amount, inventor_name FROM ads_refunds_full WHERE stat_id > 64\")\n", "labels": {"reads": [{"table": "ads_refunds_full", "columns": ["stat_id", "investment_amount", "inventor_name"]}], "writes": [{"table": "fashion_trend_data", "columns": ["stat_id", "investment_amount", "inventor_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 134;\nSQL\n", "labels": {"reads": [{"table": "artistsales", "columns": ["recycler_id", "communityid"]}, {"table": "musicgenre", "columns": ["milliseconds", "sentence_length", "role_code", "group_equity_shareholding"]}], "writes": [{"table": "development_hours", "columns": ["milliseconds", "sentence_length", "role_code", "group_equity_shareholding"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO dws.dws_users_hourly SELECT hourid, opponent_id, interest_group FROM criminal_justice_reform_initiatives WHERE hourid > 247\")\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": ["hourid", "opponent_id", "interest_group"]}], "writes": [{"table": "dws.dws_users_hourly", "columns": ["hourid", "opponent_id", "interest_group"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO colorado_river_basin SELECT issue_date, price FROM dws.coupon_use_di WHERE issue_date > 153\")\n", "labels": {"reads": [{"table": "dws.coupon_use_di", "columns": ["issue_date", "price"]}], "writes": [{"table": "colorado_river_basin", "columns": ["issue_date", "price"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM chemicals_annual\", conn)\ndf.to_sql(\"dw.dw_sessions_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": null}], "writes": [{"table": "dw.dw_sessions_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO sportsinfo SELECT donorid, amount_claimed, made_in_usa, delivery_time FROM efforts WHERE donorid > 310\"\n", "labels": {"reads": [{"table": "efforts", "columns": ["donorid", "amount_claimed", "made_in_usa", "delivery_time"]}], "writes": [{"table": "sportsinfo", "columns": ["donorid", "amount_claimed", "made_in_usa", "delivery_time"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO ads.sessions_hourly SELECT quality, lieutenant_governor, order_shipping_charges, report_id FROM user_profiles WHERE quality > 204\")\n", "labels": {"reads": [{"table": "user_profiles", "columns": ["quality", "lieutenant_governor", "order_shipping_charges", "report_id"]}], "writes": [{"table": "ads.sessions_hourly", "columns": ["quality", "lieutenant_governor", "order_shipping_charges", "report_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO art_pieces SELECT a.project_category, b.card_id FROM human_resources a JOIN underwater_cables b ON a.sighting_date = b.sighting_date\"\n", "labels": {"reads": [{"table": "human_resources", "columns": null}, {"table": "underwater_cables", "columns": null}], "writes": [{"table": "art_pieces", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 185;\nEOF\n", "labels": {"reads": [{"table": "professionals", "columns": ["f_id", "deliverydate", "ai_algorithm_id", "screening_id"]}], "writes": [{"table": "all_documents", "columns": ["f_id", "deliverydate", "ai_algorithm_id", "screening_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM navalvessels\"\n", "labels": {"reads": [{"table": "navalvessels", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"dwd.dwd_cart_item_di\")\npersist_to_output(df, \"flu_shots\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": null}], "writes": [{"table": "flu_shots", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table africa_schema.african_mines --columns wastetype,facultyid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "africa_schema.african_mines", "columns": ["wastetype", "facultyid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"apac_hotel_views\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO dispensarysales SELECT lot_details, company_type_code FROM battery_projects WHERE lot_details > 45\")\n", "labels": {"reads": [{"table": "battery_projects", "columns": ["lot_details", "company_type_code"]}], "writes": [{"table": "dispensarysales", "columns": ["lot_details", "company_type_code"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT license_type, rural FROM space_agencies_2 LIMIT 134\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO attendance SELECT lieutenant_governor, production_value FROM city_tech WHERE lieutenant_governor > 282\")\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": ["license_type", "rural"]}, {"table": "city_tech", "columns": ["lieutenant_governor", "production_value"]}], "writes": [{"table": "attendance", "columns": ["lieutenant_governor", "production_value"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO online_travel_agency SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods_risk_score_delta SELECT a.publisher, b.app_name FROM pediatricians a JOIN ods.ods_campaigns_hourly b ON a.volunteerage = b.volunteerage\"\n", "labels": {"reads": [{"table": "pediatricians", "columns": null}, {"table": "ods.ods_campaigns_hourly", "columns": null}], "writes": [{"table": "ods_risk_score_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cybersecurity_strategies SELECT healthcareid, content_id, exit_date FROM garmentproduction WHERE healthcareid > 362\"\n", "labels": {"reads": [{"table": "garmentproduction", "columns": ["healthcareid", "content_id", "exit_date"]}], "writes": [{"table": "cybersecurity_strategies", "columns": ["healthcareid", "content_id", "exit_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ihsaa_football_class, kids FROM stg.users LIMIT 329\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "stg.users", "columns": ["ihsaa_football_class", "kids"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO eventparticipation SELECT statename, emp_dob FROM farmers WHERE statename > 347\"\n", "labels": {"reads": [{"table": "farmers", "columns": ["statename", "emp_dob"]}], "writes": [{"table": "eventparticipation", "columns": ["statename", "emp_dob"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO public.trips_by_day_train SELECT call_id, citation_time, green_building_certified FROM incident_region WHERE call_id > 464\")\n", "labels": {"reads": [{"table": "incident_region", "columns": ["call_id", "citation_time", "green_building_certified"]}], "writes": [{"table": "public.trips_by_day_train", "columns": ["call_id", "citation_time", "green_building_certified"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"disabilitysupportprograms\").where(\"dt = current_date()\").writeTo(\"stg.stg_device_log_daily\").append()\n", "labels": {"reads": [{"table": "disabilitysupportprograms", "columns": null}], "writes": [{"table": "stg.stg_device_log_daily", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_risk_score_hourly\").toPandas()\ndf[[\"claim_header_id\", \"student_details\"]].to_sql(\"fish_suppliers\", engine, index=False)\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": null}], "writes": [{"table": "fish_suppliers", "columns": ["claim_header_id", "student_details"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"project_duration\")\nsrc.write.insertInto(\"music_events\", overwrite=True)\n", "labels": {"reads": [{"table": "project_duration", "columns": null}], "writes": [{"table": "music_events", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO communitypolicingcenters SELECT calendar, member, invoice_date FROM mart.shipments_full WHERE calendar > 38\"\n", "labels": {"reads": [{"table": "mart.shipments_full", "columns": ["calendar", "member", "invoice_date"]}], "writes": [{"table": "communitypolicingcenters", "columns": ["calendar", "member", "invoice_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO traditional_arts SELECT issue_count, publicationid, healthequitymetricscore, customer_number FROM employee WHERE issue_count > 67\"], check=True)\n", "labels": {"reads": [{"table": "employee", "columns": ["issue_count", "publicationid", "healthequitymetricscore", "customer_number"]}], "writes": [{"table": "traditional_arts", "columns": ["issue_count", "publicationid", "healthequitymetricscore", "customer_number"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT booking_id, attraction_name FROM bi.bi_inventory_full LIMIT 102\")\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO patienttreatments SELECT services, state, enable_third_party_ads FROM song WHERE services > 137\")\n", "labels": {"reads": [{"table": "bi.bi_inventory_full", "columns": ["booking_id", "attraction_name"]}, {"table": "song", "columns": ["services", "state", "enable_third_party_ads"]}], "writes": [{"table": "patienttreatments", "columns": ["services", "state", "enable_third_party_ads"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sample_date, dish FROM stg.stg_risk_score\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"geneva_motor_show\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": ["sample_date", "dish"]}], "writes": [{"table": "geneva_motor_show", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bridgerainfall\").where(\"dt = current_date()\").writeTo(\"water_distribution\").append()\n", "labels": {"reads": [{"table": "bridgerainfall", "columns": null}], "writes": [{"table": "water_distribution", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO us_military_personnel SELECT part_fault_id, license_number, workers FROM train WHERE part_fault_id > 367\")\n", "labels": {"reads": [{"table": "train", "columns": ["part_fault_id", "license_number", "workers"]}], "writes": [{"table": "us_military_personnel", "columns": ["part_fault_id", "license_number", "workers"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO initiatives_3 (council_tax_id, race) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "initiatives_3", "columns": ["council_tax_id", "race"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_source(ctx, \"manufacturingplants\")\npersist_to_output(df, \"clothingsales\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "manufacturingplants", "columns": null}], "writes": [{"table": "clothingsales", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"field_rainfall\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"climate_monitoring_stations\")\n", "labels": {"reads": [{"table": "field_rainfall", "columns": null}], "writes": [{"table": "climate_monitoring_stations", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT flightid, architect_id FROM neighborhoods LIMIT 208\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO schools SELECT cinema_id, tot_cred FROM tasks WHERE cinema_id > 282\")\n", "labels": {"reads": [{"table": "neighborhoods", "columns": ["flightid", "architect_id"]}, {"table": "tasks", "columns": ["cinema_id", "tot_cred"]}], "writes": [{"table": "schools", "columns": ["cinema_id", "tot_cred"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO authenticationlogs SELECT hourlyrate, student_id, shipment_id FROM nailpolishsales WHERE hourlyrate > 359\"\n", "labels": {"reads": [{"table": "nailpolishsales", "columns": ["hourlyrate", "student_id", "shipment_id"]}], "writes": [{"table": "authenticationlogs", "columns": ["hourlyrate", "student_id", "shipment_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.studentid > 100).all()\n# src table: exoplanet_discoveries\nengine.execute(\"INSERT INTO school_districts SELECT * FROM exoplanet_discoveries\")\n", "labels": {"reads": [{"table": "exoplanet_discoveries", "columns": null}], "writes": [{"table": "school_districts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM contractnegotiations\"\n", "labels": {"reads": [{"table": "contractnegotiations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO bi.bi_shipments SELECT market_value, focal_length_mm FROM fashion_trend_data WHERE market_value > 302\")\n", "labels": {"reads": [{"table": "fashion_trend_data", "columns": ["market_value", "focal_length_mm"]}], "writes": [{"table": "bi.bi_shipments", "columns": ["market_value", "focal_length_mm"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.app_name > 281).all()\n# src table: manufacturing_processes\nengine.execute(\"INSERT INTO vessel_capacity SELECT * FROM manufacturing_processes\")\n", "labels": {"reads": [{"table": "manufacturing_processes", "columns": null}], "writes": [{"table": "vessel_capacity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT council_id, is_electric FROM weapons\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"dws.inventory_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "weapons", "columns": ["council_id", "is_electric"]}], "writes": [{"table": "dws.inventory_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicle_maintenance\").toPandas()\ndf[[\"stories\", \"device\"]].to_sql(\"educators\", engine, index=False)\n", "labels": {"reads": [{"table": "vehicle_maintenance", "columns": null}], "writes": [{"table": "educators", "columns": ["stories", "device"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 351;\nSQL\n", "labels": {"reads": [{"table": "supplier_addresses", "columns": ["mh_id", "water_type"]}, {"table": "workforce", "columns": ["supplier_company_id", "ai_id", "asset_model", "average_age"]}], "writes": [{"table": "rent_arrears", "columns": ["supplier_company_id", "ai_id", "asset_model", "average_age"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO producers SELECT a.team_name, b.co_owner_count FROM biosensors.patents a JOIN departments b ON a.dept_address = b.dept_address\"\n", "labels": {"reads": [{"table": "biosensors.patents", "columns": null}, {"table": "departments", "columns": null}], "writes": [{"table": "producers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO consumer SELECT low_income_neighborhood, teamname, athlete FROM building_stats WHERE low_income_neighborhood > 98\"\n", "labels": {"reads": [{"table": "building_stats", "columns": ["low_income_neighborhood", "teamname", "athlete"]}], "writes": [{"table": "consumer", "columns": ["low_income_neighborhood", "teamname", "athlete"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO art_collection SELECT floor_area_m2, silver, track_id FROM ocean WHERE floor_area_m2 > 6\"\n", "labels": {"reads": [{"table": "ocean", "columns": ["floor_area_m2", "silver", "track_id"]}], "writes": [{"table": "art_collection", "columns": ["floor_area_m2", "silver", "track_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"emergency_categories\")\nsink_to_warehouse(df, \"spending\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "emergency_categories", "columns": null}], "writes": [{"table": "spending", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO university SELECT company, date_joined_staff FROM defense_diplomacy WHERE company > 330\");\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": ["company", "date_joined_staff"]}], "writes": [{"table": "university", "columns": ["company", "date_joined_staff"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO accommodations SELECT trainingdate, payment_method_code FROM dw_vendors_di WHERE trainingdate > 70\"\n", "labels": {"reads": [{"table": "dw_vendors_di", "columns": ["trainingdate", "payment_method_code"]}], "writes": [{"table": "accommodations", "columns": ["trainingdate", "payment_method_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM instructors\"\n", "labels": {"reads": [{"table": "instructors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.num_volunteers > 473).all()\n# src table: militaryinnovations\nengine.execute(\"INSERT INTO media_types SELECT * FROM militaryinnovations\")\n", "labels": {"reads": [{"table": "militaryinnovations", "columns": null}], "writes": [{"table": "media_types", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO london.stations SELECT a.request, b.maintenance_contract_id FROM bi.bi_events_full a JOIN cosmetic_sales b ON a.timestamp = b.timestamp\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}, {"table": "cosmetic_sales", "columns": null}], "writes": [{"table": "london.stations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO ods.inventory_df SELECT bandmate, contractor FROM threat_intelligence_budget WHERE bandmate > 149\")\n", "labels": {"reads": [{"table": "threat_intelligence_budget", "columns": ["bandmate", "contractor"]}], "writes": [{"table": "ods.inventory_df", "columns": ["bandmate", "contractor"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO wastedata SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hours_played, account_number FROM species_observations\", engine)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"login_attempts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "species_observations", "columns": ["hours_played", "account_number"]}], "writes": [{"table": "login_attempts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart_refunds\"\n", "labels": {"reads": [{"table": "mart_refunds", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO ads.ads_vendors_hourly SELECT base_name, garment_name, staff_name, workers FROM ods.ods_member_point_delta WHERE base_name > 185\")\n", "labels": {"reads": [{"table": "ods.ods_member_point_delta", "columns": ["base_name", "garment_name", "staff_name", "workers"]}], "writes": [{"table": "ads.ads_vendors_hourly", "columns": ["base_name", "garment_name", "staff_name", "workers"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO fleet SELECT fish_id, donorage FROM instructors WHERE fish_id > 454\")\n", "labels": {"reads": [{"table": "instructors", "columns": ["fish_id", "donorage"]}], "writes": [{"table": "fleet", "columns": ["fish_id", "donorage"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO team_revenue (closuredate, reservoir_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "team_revenue", "columns": ["closuredate", "reservoir_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO open_pedagogy_enrollment SELECT city_traffic_speed, album_id FROM autoshow WHERE city_traffic_speed > 228\"\n", "labels": {"reads": [{"table": "autoshow", "columns": ["city_traffic_speed", "album_id"]}], "writes": [{"table": "open_pedagogy_enrollment", "columns": ["city_traffic_speed", "album_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gymnast\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"checking\")\n", "labels": {"reads": [{"table": "gymnast", "columns": null}], "writes": [{"table": "checking", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO medicine SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"associatedheritages\")\nsrc.write.insertInto(\"clothingitems\", overwrite=True)\n", "labels": {"reads": [{"table": "associatedheritages", "columns": null}], "writes": [{"table": "clothingitems", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads.ads_payments_hourly\"\n", "labels": {"reads": [{"table": "ads.ads_payments_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"production_rare_earth_elements\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "production_rare_earth_elements", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"trains\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"council_tax\")\n", "labels": {"reads": [{"table": "trains", "columns": null}], "writes": [{"table": "council_tax", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"broadband_subscribers\");\ndf.write().mode(\"overwrite\").saveAsTable(\"busmaintenance\");\n", "labels": {"reads": [{"table": "broadband_subscribers", "columns": null}], "writes": [{"table": "busmaintenance", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO pollution_initiatives SELECT vaccinations, suppliername FROM login_attempts WHERE vaccinations > 294\");\n", "labels": {"reads": [{"table": "login_attempts", "columns": ["vaccinations", "suppliername"]}], "writes": [{"table": "pollution_initiatives", "columns": ["vaccinations", "suppliername"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT songname, pilot_id FROM tourist_destinations LIMIT 491\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "tourist_destinations", "columns": ["songname", "pilot_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"culturalcompetency\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"animal_populations\")\n", "labels": {"reads": [{"table": "culturalcompetency", "columns": null}], "writes": [{"table": "animal_populations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart_exposure_hourly SELECT completion_date, farmname, aircraft_id, agreementid FROM vrusers WHERE completion_date > 11\"\n", "labels": {"reads": [{"table": "vrusers", "columns": ["completion_date", "farmname", "aircraft_id", "agreementid"]}], "writes": [{"table": "mart_exposure_hourly", "columns": ["completion_date", "farmname", "aircraft_id", "agreementid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart_shipments_full SELECT volunteer_date, zone_name, energy_consumption FROM stg.refunds_hourly WHERE volunteer_date > 383\"\n", "labels": {"reads": [{"table": "stg.refunds_hourly", "columns": ["volunteer_date", "zone_name", "energy_consumption"]}], "writes": [{"table": "mart_shipments_full", "columns": ["volunteer_date", "zone_name", "energy_consumption"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model shoes depends on student_courses\ndbt run --select shoes --vars 'source: student_courses'\n", "labels": {"reads": [{"table": "student_courses", "columns": null}], "writes": [{"table": "shoes", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO az_drought_impact SELECT incidents, delivery_status FROM playergamehistory WHERE incidents > 385\"\n", "labels": {"reads": [{"table": "playergamehistory", "columns": ["incidents", "delivery_status"]}], "writes": [{"table": "az_drought_impact", "columns": ["incidents", "delivery_status"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO london.stations SELECT join_year, habitat_id, gender_diversity, contract_end FROM bioprocess.engineering_projects WHERE join_year > 365\")\n", "labels": {"reads": [{"table": "bioprocess.engineering_projects", "columns": ["join_year", "habitat_id", "gender_diversity", "contract_end"]}], "writes": [{"table": "london.stations", "columns": ["join_year", "habitat_id", "gender_diversity", "contract_end"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO tickets_3 SELECT posts_per_day, year_deforested, invoice_date FROM supportprograms WHERE posts_per_day > 10\"\n", "labels": {"reads": [{"table": "supportprograms", "columns": ["posts_per_day", "year_deforested", "invoice_date"]}], "writes": [{"table": "tickets_3", "columns": ["posts_per_day", "year_deforested", "invoice_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO store_district (participatedinesports, round_number) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "store_district", "columns": ["participatedinesports", "round_number"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO community_leaders SELECT passenger_id, excavation_site_id, outcome FROM communication_scores WHERE passenger_id > 490\")\n", "labels": {"reads": [{"table": "communication_scores", "columns": ["passenger_id", "excavation_site_id", "outcome"]}], "writes": [{"table": "community_leaders", "columns": ["passenger_id", "excavation_site_id", "outcome"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"trafficviolations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tour_guides\")\n", "labels": {"reads": [{"table": "trafficviolations", "columns": null}], "writes": [{"table": "tour_guides", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads (agegroup, exit_strategy) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads", "columns": ["agegroup", "exit_strategy"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 314;\nSQL\n", "labels": {"reads": [{"table": "defense_spending_3", "columns": ["language_id", "dept_code"]}, {"table": "inspections", "columns": ["total_amount_purchased", "use_date", "dept_store_id", "nickname"]}], "writes": [{"table": "viewership", "columns": ["total_amount_purchased", "use_date", "dept_store_id", "nickname"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hires\").toPandas()\ndf[[\"total_spent\", \"exhibition_id\"]].to_sql(\"researchgrants\", engine, index=False)\n", "labels": {"reads": [{"table": "hires", "columns": null}], "writes": [{"table": "researchgrants", "columns": ["total_spent", "exhibition_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM open_pedagogy_exam\", conn)\ndf.to_sql(\"temperaturehistory\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "open_pedagogy_exam", "columns": null}], "writes": [{"table": "temperaturehistory", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sales_details > 64).all()\n# src table: salary\nengine.execute(\"INSERT INTO artpieces SELECT * FROM salary\")\n", "labels": {"reads": [{"table": "salary", "columns": null}], "writes": [{"table": "artpieces", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO employeedata SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO species_data SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"carbonoffsetinitiatives\").where(\"dt = current_date()\").writeTo(\"africa_schema.african_mines\").append()\n", "labels": {"reads": [{"table": "carbonoffsetinitiatives", "columns": null}], "writes": [{"table": "africa_schema.african_mines", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO customer_month SELECT container_id, anomaly, injured FROM impact_asia WHERE container_id > 111\");\n", "labels": {"reads": [{"table": "impact_asia", "columns": ["container_id", "anomaly", "injured"]}], "writes": [{"table": "customer_month", "columns": ["container_id", "anomaly", "injured"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"marketing_budgets\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "marketing_budgets", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.expedition_name > 154).all()\n# src table: exhibitiondetails\nengine.execute(\"INSERT INTO drama_workshop_groups SELECT * FROM exhibitiondetails\")\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": null}], "writes": [{"table": "drama_workshop_groups", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"productivity\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "productivity", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM locations\", conn)\ndf.to_sql(\"ads.ads_campaigns_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "locations", "columns": null}], "writes": [{"table": "ads.ads_campaigns_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM field\"\n", "labels": {"reads": [{"table": "field", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO government.city SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT i_id, user_login FROM teacher_pd_hours LIMIT 484\")\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO vrusers SELECT field_name, claim_status_name FROM fairtradecertifications WHERE field_name > 108\")\n", "labels": {"reads": [{"table": "teacher_pd_hours", "columns": ["i_id", "user_login"]}, {"table": "fairtradecertifications", "columns": ["field_name", "claim_status_name"]}], "writes": [{"table": "vrusers", "columns": ["field_name", "claim_status_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO eco_hotels SELECT chromosome, rebounds, has_parabens, accommodationtype FROM bi.bi_sessions_hourly WHERE chromosome > 99\")\n", "labels": {"reads": [{"table": "bi.bi_sessions_hourly", "columns": ["chromosome", "rebounds", "has_parabens", "accommodationtype"]}], "writes": [{"table": "eco_hotels", "columns": ["chromosome", "rebounds", "has_parabens", "accommodationtype"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"entrepreneur\").where(\"dt = current_date()\").writeTo(\"medical_professionals\").append()\n", "labels": {"reads": [{"table": "entrepreneur", "columns": null}], "writes": [{"table": "medical_professionals", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table flu_cases --target-dir /tmp/land\n", "labels": {"reads": [{"table": "flu_cases", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"goals\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"experience\")\n", "labels": {"reads": [{"table": "goals", "columns": null}], "writes": [{"table": "experience", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO irrigation_systems SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.average_attendance > 380).all()\n# src table: ads.ads_risk_score_hourly\nengine.execute(\"INSERT INTO displaced_people SELECT * FROM ads.ads_risk_score_hourly\")\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": null}], "writes": [{"table": "displaced_people", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO stg.refunds_daily SELECT subject_name, energy_production, fleet_series FROM ocean_acidification_antarctic WHERE subject_name > 269\");\n", "labels": {"reads": [{"table": "ocean_acidification_antarctic", "columns": ["subject_name", "energy_production", "fleet_series"]}], "writes": [{"table": "stg.refunds_daily", "columns": ["subject_name", "energy_production", "fleet_series"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 447;\nEOF\n", "labels": {"reads": [{"table": "investor", "columns": ["mean_visibility_miles", "last_maintenance_date", "sustainability_rating", "valuation"]}], "writes": [{"table": "fairtradecertification", "columns": ["mean_visibility_miles", "last_maintenance_date", "sustainability_rating", "valuation"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO fish_stock SELECT a.community, b.employee_id FROM initiative_types a JOIN ap_budget b ON a.patientid = b.patientid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "initiative_types", "columns": null}, {"table": "ap_budget", "columns": null}], "writes": [{"table": "fish_stock", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT agency_id, violation_type FROM ytterbium_supply\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"artcollection\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": ["agency_id", "violation_type"]}], "writes": [{"table": "artcollection", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO drama_workshop_groups SELECT a.permit_number, b.is_organic FROM artsales a JOIN waste b ON a.decor = b.decor\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "artsales", "columns": null}, {"table": "waste", "columns": null}], "writes": [{"table": "drama_workshop_groups", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM animal_rehab\"\n", "labels": {"reads": [{"table": "animal_rehab", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO art SELECT camera_lens_id, farmid, building_phone, postal_code FROM book WHERE camera_lens_id > 344\")\n", "labels": {"reads": [{"table": "book", "columns": ["camera_lens_id", "farmid", "building_phone", "postal_code"]}], "writes": [{"table": "art", "columns": ["camera_lens_id", "farmid", "building_phone", "postal_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"marketing_regions\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"accessible_tech_categories\")\n", "labels": {"reads": [{"table": "marketing_regions", "columns": null}], "writes": [{"table": "accessible_tech_categories", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO global_sales_2022 SELECT taxi_model, involved_in_lifelong_learning, claim_stage_id, expedition_name FROM spacecraft_components WHERE taxi_model > 302\")\n", "labels": {"reads": [{"table": "spacecraft_components", "columns": ["taxi_model", "involved_in_lifelong_learning", "claim_stage_id", "expedition_name"]}], "writes": [{"table": "global_sales_2022", "columns": ["taxi_model", "involved_in_lifelong_learning", "claim_stage_id", "expedition_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"diversity\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "diversity", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO fairtradecertification SELECT a.is_organic, b.startyear FROM nz_tourism a JOIN ref_shipping_agents b ON a.stadium_id = b.stadium_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "nz_tourism", "columns": null}, {"table": "ref_shipping_agents", "columns": null}], "writes": [{"table": "fairtradecertification", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table tickets --columns attendance_date,credits --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "tickets", "columns": ["attendance_date", "credits"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO biotech_startups SELECT total_employees, batting_average, container_count, productname FROM staff_roles WHERE total_employees > 74\");\n", "labels": {"reads": [{"table": "staff_roles", "columns": ["total_employees", "batting_average", "container_count", "productname"]}], "writes": [{"table": "biotech_startups", "columns": ["total_employees", "batting_average", "container_count", "productname"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO genetics.projects SELECT * FROM legacy\nspark.sql(\"INSERT INTO carbon_pricing SELECT customer_country, excavation_site_id FROM ods_vendors_daily WHERE customer_country > 157\")\n", "labels": {"reads": [{"table": "ods_vendors_daily", "columns": ["customer_country", "excavation_site_id"]}], "writes": [{"table": "carbon_pricing", "columns": ["customer_country", "excavation_site_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"soil_moisture\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"chemical_composition\")\n", "labels": {"reads": [{"table": "soil_moisture", "columns": null}], "writes": [{"table": "chemical_composition", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO diseases SELECT * FROM legacy\ncur.execute(\"SELECT population, specialty FROM wastedata LIMIT 53\")\n", "labels": {"reads": [{"table": "wastedata", "columns": ["population", "specialty"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.developer > 31).all()\n# src table: bi.bi_payments_full\nengine.execute(\"INSERT INTO stg.cart_item_full SELECT * FROM bi.bi_payments_full\")\n", "labels": {"reads": [{"table": "bi.bi_payments_full", "columns": null}], "writes": [{"table": "stg.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO media_types SELECT * FROM legacy\ncur.execute(\"SELECT student_details, log_entry_date FROM carbon_prices LIMIT 416\")\n", "labels": {"reads": [{"table": "carbon_prices", "columns": ["student_details", "log_entry_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"section\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"canada_cosmetics_preferences\")\n", "labels": {"reads": [{"table": "section", "columns": null}], "writes": [{"table": "canada_cosmetics_preferences", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bustrips\").toPandas()\ndf[[\"fan_name\", \"accreditation_level\"]].to_sql(\"daily_production\", engine, index=False)\n", "labels": {"reads": [{"table": "bustrips", "columns": null}], "writes": [{"table": "daily_production", "columns": ["fan_name", "accreditation_level"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO customerorders SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ethical_manufacturing > 244).all()\n# src table: forestry_practices\nengine.execute(\"INSERT INTO acidification_data SELECT * FROM forestry_practices\")\n", "labels": {"reads": [{"table": "forestry_practices", "columns": null}], "writes": [{"table": "acidification_data", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO spacecraft_manufacturers SELECT a.communityname, b.materialid FROM purchase a JOIN shariah_financing b ON a.date_closed = b.date_closed\"\n", "labels": {"reads": [{"table": "purchase", "columns": null}, {"table": "shariah_financing", "columns": null}], "writes": [{"table": "spacecraft_manufacturers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO tracks SELECT team_id_loser, ratingdate FROM ods.ods_clicks_di WHERE team_id_loser > 385\"\n", "labels": {"reads": [{"table": "ods.ods_clicks_di", "columns": ["team_id_loser", "ratingdate"]}], "writes": [{"table": "tracks", "columns": ["team_id_loser", "ratingdate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.review_rating > 90).all()\n# src table: legislation\nengine.execute(\"INSERT INTO carbon_prices SELECT * FROM legislation\")\n", "labels": {"reads": [{"table": "legislation", "columns": null}], "writes": [{"table": "carbon_prices", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO artists SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 289;\nSQL\n", "labels": {"reads": [{"table": "research_grants", "columns": ["threat_type", "contributor"]}, {"table": "professionals", "columns": ["min_dew_point_f", "order_date", "language"]}], "writes": [{"table": "member", "columns": ["min_dew_point_f", "order_date", "language"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO department_publications SELECT rec_engine, maintenancedate, updated_at FROM product_review WHERE rec_engine > 284\")\n", "labels": {"reads": [{"table": "product_review", "columns": ["rec_engine", "maintenancedate", "updated_at"]}], "writes": [{"table": "department_publications", "columns": ["rec_engine", "maintenancedate", "updated_at"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO researchgrants SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO livestock SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO state_usage SELECT production_usage, mh_id FROM dws.inventory_daily WHERE production_usage > 14\")\n", "labels": {"reads": [{"table": "dws.inventory_daily", "columns": ["production_usage", "mh_id"]}], "writes": [{"table": "state_usage", "columns": ["production_usage", "mh_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO movie (menu_category, has_spf) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "movie", "columns": ["menu_category", "has_spf"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO branch SELECT discovery_date, activity, highscore, email_address FROM canada_cosmetics_preferences WHERE discovery_date > 297\"\n", "labels": {"reads": [{"table": "canada_cosmetics_preferences", "columns": ["discovery_date", "activity", "highscore", "email_address"]}], "writes": [{"table": "branch", "columns": ["discovery_date", "activity", "highscore", "email_address"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"inclusive_housing\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "inclusive_housing", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wellbeing_programs SELECT * FROM legacy\ncur.execute(\"SELECT cmi_cross_ref_id, socialimpactscore FROM public_participation LIMIT 368\")\n", "labels": {"reads": [{"table": "public_participation", "columns": ["cmi_cross_ref_id", "socialimpactscore"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT performancedate, range FROM waterconservationbudget LIMIT 451\")\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO stg.stg_campaigns_hourly SELECT invoice_details, publication_year, workout_date FROM landfillcapacitybycountry WHERE invoice_details > 442\")\n", "labels": {"reads": [{"table": "waterconservationbudget", "columns": ["performancedate", "range"]}, {"table": "landfillcapacitybycountry", "columns": ["invoice_details", "publication_year", "workout_date"]}], "writes": [{"table": "stg.stg_campaigns_hourly", "columns": ["invoice_details", "publication_year", "workout_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM schoolc\", conn)\ndf.to_sql(\"researchgrants\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "schoolc", "columns": null}], "writes": [{"table": "researchgrants", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO libraries SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO org_volunteer SELECT * FROM legacy\nspark.sql(\"INSERT INTO therapy SELECT yield, end_date, co_owner_count, payment_id FROM vessel_registry WHERE yield > 247\")\n", "labels": {"reads": [{"table": "vessel_registry", "columns": ["yield", "end_date", "co_owner_count", "payment_id"]}], "writes": [{"table": "therapy", "columns": ["yield", "end_date", "co_owner_count", "payment_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO film_category SELECT a.address_line_2, b.founding_year FROM online_platform a JOIN hospitallocations b ON a.num_of_staff = b.num_of_staff\"\n", "labels": {"reads": [{"table": "online_platform", "columns": null}, {"table": "hospitallocations", "columns": null}], "writes": [{"table": "film_category", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM redundant_billing_data\", conn)\ndf.to_sql(\"athlete_stats\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "redundant_billing_data", "columns": null}], "writes": [{"table": "athlete_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mine_workforce SELECT base_name, mineid, station_name FROM airport WHERE base_name > 201\"\n", "labels": {"reads": [{"table": "airport", "columns": ["base_name", "mineid", "station_name"]}], "writes": [{"table": "mine_workforce", "columns": ["base_name", "mineid", "station_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO investment_strategies SELECT organic_matter, sector, login_name FROM labor_stats WHERE organic_matter > 79\"\n", "labels": {"reads": [{"table": "labor_stats", "columns": ["organic_matter", "sector", "login_name"]}], "writes": [{"table": "investment_strategies", "columns": ["organic_matter", "sector", "login_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicle\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"iron_ore_production\")\n", "labels": {"reads": [{"table": "vehicle", "columns": null}], "writes": [{"table": "iron_ore_production", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO sustainableprojects SELECT player_id, acc_type FROM bookings WHERE player_id > 413\");\n", "labels": {"reads": [{"table": "bookings", "columns": ["player_id", "acc_type"]}], "writes": [{"table": "sustainableprojects", "columns": ["player_id", "acc_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO miningoperations SELECT studentname, lesson_status_code, strategy_name FROM stg.stg_coupon_use_hourly WHERE studentname > 31\");\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_hourly", "columns": ["studentname", "lesson_status_code", "strategy_name"]}], "writes": [{"table": "miningoperations", "columns": ["studentname", "lesson_status_code", "strategy_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 243;\nEOF\n", "labels": {"reads": [{"table": "rental", "columns": ["attack_date", "party_id", "principal_activities", "workerid"]}], "writes": [{"table": "chemical_production_5", "columns": ["attack_date", "party_id", "principal_activities", "workerid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 163;\nSQL\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["workoutname", "founder_count"]}, {"table": "stg.stg_shipments_hourly", "columns": ["transaction_amount", "state"]}], "writes": [{"table": "countries", "columns": ["transaction_amount", "state"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_input(ctx, \"developers\")\npersist_to_sink(df, \"road_construction\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "developers", "columns": null}], "writes": [{"table": "road_construction", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"workshops\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws.dws_coupon_use_hourly\");\n", "labels": {"reads": [{"table": "workshops", "columns": null}], "writes": [{"table": "dws.dws_coupon_use_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT school_name, editor_id FROM middle_east_military_spending LIMIT 159\")\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO chemical_processes SELECT amount_of_refund, potency, medium FROM document_functional_areas WHERE amount_of_refund > 86\")\n", "labels": {"reads": [{"table": "middle_east_military_spending", "columns": ["school_name", "editor_id"]}, {"table": "document_functional_areas", "columns": ["amount_of_refund", "potency", "medium"]}], "writes": [{"table": "chemical_processes", "columns": ["amount_of_refund", "potency", "medium"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO user_video_view SELECT investorgender, heritage_site_id, chromosome FROM landfill_capacity_city_v2 WHERE investorgender > 312\");\n", "labels": {"reads": [{"table": "landfill_capacity_city_v2", "columns": ["investorgender", "heritage_site_id", "chromosome"]}], "writes": [{"table": "user_video_view", "columns": ["investorgender", "heritage_site_id", "chromosome"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.time_stamp > 411).all()\n# src table: document_structures\nengine.execute(\"INSERT INTO ocean_shipping.cargo SELECT * FROM document_structures\")\n", "labels": {"reads": [{"table": "document_structures", "columns": null}], "writes": [{"table": "ocean_shipping.cargo", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"grant\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"maintenance\")\n", "labels": {"reads": [{"table": "grant", "columns": null}], "writes": [{"table": "maintenance", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mobile_usage SELECT * FROM legacy\ncur.execute(\"SELECT call_date, investment_amount FROM employee LIMIT 394\")\n", "labels": {"reads": [{"table": "employee", "columns": ["call_date", "investment_amount"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table landfill_capacity --columns alid,ratingdate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "landfill_capacity", "columns": ["alid", "ratingdate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO cybersecurityincidents SELECT total_employees, recipient FROM eco_hotels WHERE total_employees > 69\");\n", "labels": {"reads": [{"table": "eco_hotels", "columns": ["total_employees", "recipient"]}], "writes": [{"table": "cybersecurityincidents", "columns": ["total_employees", "recipient"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO carbon_emissions SELECT vesselname, local_authority, causeid, project_category FROM mart.mart_device_log_hourly WHERE vesselname > 405\"\n", "labels": {"reads": [{"table": "mart.mart_device_log_hourly", "columns": ["vesselname", "local_authority", "causeid", "project_category"]}], "writes": [{"table": "carbon_emissions", "columns": ["vesselname", "local_authority", "causeid", "project_category"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO user_activity SELECT a.instructor_id, b.num_pallets FROM sustainablebrands a JOIN lead_mines b ON a.birth_country = b.birth_country\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": null}, {"table": "lead_mines", "columns": null}], "writes": [{"table": "user_activity", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 85;\nSQL\n", "labels": {"reads": [{"table": "manager_award", "columns": ["session_language", "policytype"]}, {"table": "performers", "columns": ["mental_health_score", "stu_dob", "outcome_date", "temporary_acting"]}], "writes": [{"table": "artcontributors", "columns": ["mental_health_score", "stu_dob", "outcome_date", "temporary_acting"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"low_value_contracts\").toPandas()\ndf[[\"physical\", \"is_recycled\"]].to_sql(\"buildings\", engine, index=False)\n", "labels": {"reads": [{"table": "low_value_contracts", "columns": null}], "writes": [{"table": "buildings", "columns": ["physical", "is_recycled"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ods.inventory_df (sensor_id, is_commercial) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ods.inventory_df", "columns": ["sensor_id", "is_commercial"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"caribbean_tourists\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"trees\")\n", "labels": {"reads": [{"table": "caribbean_tourists", "columns": null}], "writes": [{"table": "trees", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bike_share\");\ndf.write().mode(\"overwrite\").saveAsTable(\"stg.stg_shipments_hourly\");\n", "labels": {"reads": [{"table": "bike_share", "columns": null}], "writes": [{"table": "stg.stg_shipments_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO military_technology_projects SELECT a.quantity_containers, b.ingredient FROM eventlocations a JOIN acidification_data b ON a.train_number = b.train_number\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "eventlocations", "columns": null}, {"table": "acidification_data", "columns": null}], "writes": [{"table": "military_technology_projects", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO factories_africa (artwork_name, tourist_details) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "factories_africa", "columns": ["artwork_name", "tourist_details"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"acceptance\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_member_point_df\")\n", "labels": {"reads": [{"table": "acceptance", "columns": null}], "writes": [{"table": "dws.dws_member_point_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO rural_areas SELECT crs_credit, digital_channel, warehouse_state FROM match_result WHERE crs_credit > 309\");\n", "labels": {"reads": [{"table": "match_result", "columns": ["crs_credit", "digital_channel", "warehouse_state"]}], "writes": [{"table": "rural_areas", "columns": ["crs_credit", "digital_channel", "warehouse_state"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO donations2022 SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"instructor\").where(\"dt = current_date()\").writeTo(\"route\").append()\n", "labels": {"reads": [{"table": "instructor", "columns": null}], "writes": [{"table": "route", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO voting_record SELECT archaeologist_name, service_name, crossing, artist_gender FROM enzyme WHERE archaeologist_name > 242\"\n", "labels": {"reads": [{"table": "enzyme", "columns": ["archaeologist_name", "service_name", "crossing", "artist_gender"]}], "writes": [{"table": "voting_record", "columns": ["archaeologist_name", "service_name", "crossing", "artist_gender"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_agriculture_initiatives\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "urban_agriculture_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO parking_fines SELECT area_type, dorm_name, prereq_id, heritage_site_id FROM agri_innov WHERE area_type > 199\")\n", "labels": {"reads": [{"table": "agri_innov", "columns": ["area_type", "dorm_name", "prereq_id", "heritage_site_id"]}], "writes": [{"table": "parking_fines", "columns": ["area_type", "dorm_name", "prereq_id", "heritage_site_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO state_budget SELECT co2_emission, crs_description FROM distributors WHERE co2_emission > 411\")\n", "labels": {"reads": [{"table": "distributors", "columns": ["co2_emission", "crs_description"]}], "writes": [{"table": "state_budget", "columns": ["co2_emission", "crs_description"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO organic_cosmetics SELECT retail_price, contract_name, attorneyid, principal_activities FROM arcticwildlifereserve WHERE retail_price > 176\");\n", "labels": {"reads": [{"table": "arcticwildlifereserve", "columns": ["retail_price", "contract_name", "attorneyid", "principal_activities"]}], "writes": [{"table": "organic_cosmetics", "columns": ["retail_price", "contract_name", "attorneyid", "principal_activities"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stock\");\ndf.write().mode(\"overwrite\").saveAsTable(\"education_programs\");\n", "labels": {"reads": [{"table": "stock", "columns": null}], "writes": [{"table": "education_programs", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"unions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "unions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO languagesatrisk (album_id, took_office) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "languagesatrisk", "columns": ["album_id", "took_office"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model vessel_registry depends on dwd.dwd_products\ndbt build --select vessel_registry --vars '{\"src\":\"dwd.dwd_products\"}'\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": null}], "writes": [{"table": "vessel_registry", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO green_certification SELECT habitat_id, applicant, gtype FROM round WHERE habitat_id > 376\")\n", "labels": {"reads": [{"table": "round", "columns": ["habitat_id", "applicant", "gtype"]}], "writes": [{"table": "green_certification", "columns": ["habitat_id", "applicant", "gtype"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"continent\")\nwrite_to_sink(df, \"cargos\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "continent", "columns": null}], "writes": [{"table": "cargos", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_table(ctx, \"mart_orders_di\")\nexport_to_store(df, \"landfill_capacity_city_v2\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart_orders_di", "columns": null}], "writes": [{"table": "landfill_capacity_city_v2", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"public.ev_sales\");\ndf.write().mode(\"overwrite\").saveAsTable(\"chip_model\");\n", "labels": {"reads": [{"table": "public.ev_sales", "columns": null}], "writes": [{"table": "chip_model", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.risk_score SELECT advocate_name, review_score, art_type, president_vote FROM lenders WHERE advocate_name > 48\"\n", "labels": {"reads": [{"table": "lenders", "columns": ["advocate_name", "review_score", "art_type", "president_vote"]}], "writes": [{"table": "ads.risk_score", "columns": ["advocate_name", "review_score", "art_type", "president_vote"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"storage_tech\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"stg.campaigns_daily\")\n", "labels": {"reads": [{"table": "storage_tech", "columns": null}], "writes": [{"table": "stg.campaigns_daily", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"parking_fines\").toPandas()\ndf[[\"player_api_id\", \"school_id\"]].to_sql(\"canals\", engine, index=False)\n", "labels": {"reads": [{"table": "parking_fines", "columns": null}], "writes": [{"table": "canals", "columns": ["player_api_id", "school_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"heart_rate_data\");\ndf.write().mode(\"overwrite\").saveAsTable(\"mart.mart_coupon_use_full\");\n", "labels": {"reads": [{"table": "heart_rate_data", "columns": null}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM store_product\", conn)\ndf.to_sql(\"aquaticfarm\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "store_product", "columns": null}], "writes": [{"table": "aquaticfarm", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO carbon_offset_programs SELECT a.mountain_id, b.stockid FROM surveylocations a JOIN waste_generation_metrics b ON a.pet_age = b.pet_age\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "surveylocations", "columns": null}, {"table": "waste_generation_metrics", "columns": null}], "writes": [{"table": "carbon_offset_programs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"player_coach\")\nwrite_to_target(df, \"mental_health_parity\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "player_coach", "columns": null}], "writes": [{"table": "mental_health_parity", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO operate_company SELECT * FROM legacy\ncur.execute(\"SELECT sale_revenue, organic_ingredients_percentage FROM asteroids LIMIT 89\")\n", "labels": {"reads": [{"table": "asteroids", "columns": ["sale_revenue", "organic_ingredients_percentage"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO dependent SELECT all_games, passenger_count FROM project_issues WHERE all_games > 310\")\n", "labels": {"reads": [{"table": "project_issues", "columns": ["all_games", "passenger_count"]}], "writes": [{"table": "dependent", "columns": ["all_games", "passenger_count"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads.ads_products_full SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT delivery_time, issue_date FROM production LIMIT 78\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "production", "columns": ["delivery_time", "issue_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO marine_species_arctic_ocean SELECT gametype, school_id, model_name FROM volume WHERE gametype > 412\");\n", "labels": {"reads": [{"table": "volume", "columns": ["gametype", "school_id", "model_name"]}], "writes": [{"table": "marine_species_arctic_ocean", "columns": ["gametype", "school_id", "model_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dws.events SELECT * FROM legacy\nspark.sql(\"INSERT INTO rainfall_data SELECT occupancy_rate, health_equity_metric_1, founder FROM climate_monitoring_stations WHERE occupancy_rate > 14\")\n", "labels": {"reads": [{"table": "climate_monitoring_stations", "columns": ["occupancy_rate", "health_equity_metric_1", "founder"]}], "writes": [{"table": "rainfall_data", "columns": ["occupancy_rate", "health_equity_metric_1", "founder"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sustainable_materials\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ocean_shipping.cargo\")\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": null}], "writes": [{"table": "ocean_shipping.cargo", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO habitat SELECT council_tax_id, shipping_mode, instid, operationname FROM staff_members WHERE council_tax_id > 463\"\n", "labels": {"reads": [{"table": "staff_members", "columns": ["council_tax_id", "shipping_mode", "instid", "operationname"]}], "writes": [{"table": "habitat", "columns": ["council_tax_id", "shipping_mode", "instid", "operationname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dws.risk_score_daily SELECT dapp_name, destruction_authorised_by_employee_id, coverage_type FROM defense_contracts WHERE dapp_name > 273\"\n", "labels": {"reads": [{"table": "defense_contracts", "columns": ["dapp_name", "destruction_authorised_by_employee_id", "coverage_type"]}], "writes": [{"table": "dws.risk_score_daily", "columns": ["dapp_name", "destruction_authorised_by_employee_id", "coverage_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO indigenouscommunities SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"representative\");\ndf.write().mode(\"overwrite\").saveAsTable(\"mart.campaigns_full\");\n", "labels": {"reads": [{"table": "representative", "columns": null}], "writes": [{"table": "mart.campaigns_full", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"drug_approvals\");\ndf.write().mode(\"overwrite\").saveAsTable(\"chip_model\");\n", "labels": {"reads": [{"table": "drug_approvals", "columns": null}], "writes": [{"table": "chip_model", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table bank_info --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bank_info", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table coal_reserves --target-dir /tmp/land\n", "labels": {"reads": [{"table": "coal_reserves", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO drilling_rigs SELECT a.other_details, b.width FROM bioprocesses a JOIN ods_vendors_daily b ON a.individual_first_name = b.individual_first_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bioprocesses", "columns": null}, {"table": "ods_vendors_daily", "columns": null}], "writes": [{"table": "drilling_rigs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO platform SELECT class_section, platformname FROM animals WHERE class_section > 395\")\n", "labels": {"reads": [{"table": "animals", "columns": ["class_section", "platformname"]}], "writes": [{"table": "platform", "columns": ["class_section", "platformname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO mart.clicks_delta SELECT a.ppos, b.host_city_id FROM nailpolishsales a JOIN hosting_city b ON a.retailer = b.retailer\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "nailpolishsales", "columns": null}, {"table": "hosting_city", "columns": null}], "writes": [{"table": "mart.clicks_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO media_library SELECT open_year, coach_id, student_name FROM paris_real_estate WHERE open_year > 399\"\n", "labels": {"reads": [{"table": "paris_real_estate", "columns": ["open_year", "coach_id", "student_name"]}], "writes": [{"table": "media_library", "columns": ["open_year", "coach_id", "student_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model gamesales depends on contract_timeline\ndbt build --models gamesales --vars 'source: contract_timeline'\n", "labels": {"reads": [{"table": "contract_timeline", "columns": null}], "writes": [{"table": "gamesales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 426;\nSQL\n", "labels": {"reads": [{"table": "product_catalog", "columns": ["fertilizer_id", "mgr_start_date"]}, {"table": "agricultural_projects", "columns": ["wind_speed_mph", "physical", "furniture_id", "launch_agency"]}], "writes": [{"table": "acceptance", "columns": ["wind_speed_mph", "physical", "furniture_id", "launch_agency"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO vehicle_prices (daily_hire_cost, schedule) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "vehicle_prices", "columns": ["daily_hire_cost", "schedule"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO farmers_india SELECT * FROM legacy\nspark.sql(\"INSERT INTO navalvessels SELECT delivery_status, id FROM player_coach WHERE delivery_status > 139\")\n", "labels": {"reads": [{"table": "player_coach", "columns": ["delivery_status", "id"]}], "writes": [{"table": "navalvessels", "columns": ["delivery_status", "id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.amount_piad > 472).all()\n# src table: academic_publications\nengine.execute(\"INSERT INTO public.crime_types SELECT * FROM academic_publications\")\n", "labels": {"reads": [{"table": "academic_publications", "columns": null}], "writes": [{"table": "public.crime_types", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tokyo_motor_show\", conn)\ndf.to_sql(\"airport_aircraft\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tokyo_motor_show", "columns": null}], "writes": [{"table": "airport_aircraft", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"dwd.dwd_users_hourly\")\nexport_to_sink(df, \"country_waste_generation\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.dwd_users_hourly", "columns": null}], "writes": [{"table": "country_waste_generation", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO mart_exposure_di SELECT forename, refugee_name, role_name FROM bi.inventory_daily WHERE forename > 129\")\n", "labels": {"reads": [{"table": "bi.inventory_daily", "columns": ["forename", "refugee_name", "role_name"]}], "writes": [{"table": "mart_exposure_di", "columns": ["forename", "refugee_name", "role_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO manufacturing_processes SELECT roomname, union_member, cost FROM claims WHERE roomname > 290\")\n", "labels": {"reads": [{"table": "claims", "columns": ["roomname", "union_member", "cost"]}], "writes": [{"table": "manufacturing_processes", "columns": ["roomname", "union_member", "cost"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO cases SELECT people_id, room_number FROM australia_offset_programs WHERE people_id > 329\");\n", "labels": {"reads": [{"table": "australia_offset_programs", "columns": ["people_id", "room_number"]}], "writes": [{"table": "cases", "columns": ["people_id", "room_number"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws.dws_events_df\")\nsrc.write.insertInto(\"landfill_capacity_city_v2\", overwrite=True)\n", "labels": {"reads": [{"table": "dws.dws_events_df", "columns": null}], "writes": [{"table": "landfill_capacity_city_v2", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM countryintelligenceops\"\n", "labels": {"reads": [{"table": "countryintelligenceops", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO home_game SELECT platform_id, focal_length_mm, analysis_date FROM product_info WHERE platform_id > 491\")\n", "labels": {"reads": [{"table": "product_info", "columns": ["platform_id", "focal_length_mm", "analysis_date"]}], "writes": [{"table": "home_game", "columns": ["platform_id", "focal_length_mm", "analysis_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT initiative_region, tournament_id FROM tb_reports\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"menu_vendors\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["initiative_region", "tournament_id"]}], "writes": [{"table": "menu_vendors", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO seal_population SELECT outcome, cargo_weight FROM election WHERE outcome > 465\")\n", "labels": {"reads": [{"table": "election", "columns": ["outcome", "cargo_weight"]}], "writes": [{"table": "seal_population", "columns": ["outcome", "cargo_weight"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT gametype, min_depth FROM stg_payments_hourly LIMIT 226\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "stg_payments_hourly", "columns": ["gametype", "min_depth"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.energy_consumption > 420).all()\n# src table: workforce_training\nengine.execute(\"INSERT INTO funding_records SELECT * FROM workforce_training\")\n", "labels": {"reads": [{"table": "workforce_training", "columns": null}], "writes": [{"table": "funding_records", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT fault_log_entry_datetime, dept_code FROM ods.ods_clicks_di\", engine)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"climate_finance_organizations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_clicks_di", "columns": ["fault_log_entry_datetime", "dept_code"]}], "writes": [{"table": "climate_finance_organizations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO review SELECT opname, carriername, lipstick_id, number_thousands FROM customer_contact_channels WHERE opname > 414\");\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": ["opname", "carriername", "lipstick_id", "number_thousands"]}], "writes": [{"table": "review", "columns": ["opname", "carriername", "lipstick_id", "number_thousands"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"product_review\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "product_review", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"purchases\")\nsrc.write.insertInto(\"party_events\", overwrite=True)\n", "labels": {"reads": [{"table": "purchases", "columns": null}], "writes": [{"table": "party_events", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO community_development.transactions SELECT bioprocess_id, organic FROM ods.shipments_df WHERE bioprocess_id > 7\");\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": ["bioprocess_id", "organic"]}], "writes": [{"table": "community_development.transactions", "columns": ["bioprocess_id", "organic"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO vulnerabilities SELECT cause_name, import_country, totalamount FROM shared_ebikes WHERE cause_name > 118\")\n", "labels": {"reads": [{"table": "shared_ebikes", "columns": ["cause_name", "import_country", "totalamount"]}], "writes": [{"table": "vulnerabilities", "columns": ["cause_name", "import_country", "totalamount"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"commercialbuildings\").toPandas()\ndf[[\"date_payment_made\", \"pollutant_type\"]].to_sql(\"org_climate_finance\", engine, index=False)\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": null}], "writes": [{"table": "org_climate_finance", "columns": ["date_payment_made", "pollutant_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO healthcare_system SELECT * FROM legacy\nspark.sql(\"INSERT INTO waste_generation SELECT party_email, effort, song_release_year FROM medical_facilities_nyc WHERE party_email > 287\")\n", "labels": {"reads": [{"table": "medical_facilities_nyc", "columns": ["party_email", "effort", "song_release_year"]}], "writes": [{"table": "waste_generation", "columns": ["party_email", "effort", "song_release_year"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT rank, market_value_in_billion FROM nursing_homes LIMIT 412\")\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO discount_coupons SELECT warehouse_id, drug_id, hours_contributed, condition FROM savings WHERE warehouse_id > 15\")\n", "labels": {"reads": [{"table": "nursing_homes", "columns": ["rank", "market_value_in_billion"]}, {"table": "savings", "columns": ["warehouse_id", "drug_id", "hours_contributed", "condition"]}], "writes": [{"table": "discount_coupons", "columns": ["warehouse_id", "drug_id", "hours_contributed", "condition"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donor_program, s_id FROM pharmasales LIMIT 354\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "pharmasales", "columns": ["donor_program", "s_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ngo_funding depends on global_sales_2022\ndbt run -s ngo_funding --vars 'source: global_sales_2022'\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}], "writes": [{"table": "ngo_funding", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO networkdevices SELECT item_price, subscriber_type FROM chemicals WHERE item_price > 402\");\n", "labels": {"reads": [{"table": "chemicals", "columns": ["item_price", "subscriber_type"]}], "writes": [{"table": "networkdevices", "columns": ["item_price", "subscriber_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.stg_coupon_use_di SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dwd.products_hourly\"\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO cotton_source SELECT time_year, made_in_usa, award, bname FROM tournaments WHERE time_year > 234\");\n", "labels": {"reads": [{"table": "tournaments", "columns": ["time_year", "made_in_usa", "award", "bname"]}], "writes": [{"table": "cotton_source", "columns": ["time_year", "made_in_usa", "award", "bname"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model program_history depends on authors\ndbt build --select program_history --vars '{\"src\":\"authors\"}'\n", "labels": {"reads": [{"table": "authors", "columns": null}], "writes": [{"table": "program_history", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM legal_technology_funding\"\n", "labels": {"reads": [{"table": "legal_technology_funding", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"donation\");\ndf.write().mode(\"overwrite\").saveAsTable(\"schools\");\n", "labels": {"reads": [{"table": "donation", "columns": null}], "writes": [{"table": "schools", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"passenger_trips\");\ndf.write().mode(\"overwrite\").saveAsTable(\"rent_arrears\");\n", "labels": {"reads": [{"table": "passenger_trips", "columns": null}], "writes": [{"table": "rent_arrears", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO problem_log SELECT station_name, restorative_justice FROM public_transportation_sydney WHERE station_name > 297\"\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": ["station_name", "restorative_justice"]}], "writes": [{"table": "problem_log", "columns": ["station_name", "restorative_justice"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"spacecraft_manufacturing\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "spacecraft_manufacturing", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shariah_financing (activity, access_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shariah_financing", "columns": ["activity", "access_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT bioreactor_id, owner_id FROM ads_payments_hourly LIMIT 490\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": ["bioreactor_id", "owner_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dwd.coupon_use_full SELECT labor_cost, aid FROM home_game WHERE labor_cost > 188\"], check=True)\n", "labels": {"reads": [{"table": "home_game", "columns": ["labor_cost", "aid"]}], "writes": [{"table": "dwd.coupon_use_full", "columns": ["labor_cost", "aid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bikerental\").toPandas()\ndf[[\"calendar\", \"thefttypeid\"]].to_sql(\"field5\", engine, index=False)\n", "labels": {"reads": [{"table": "bikerental", "columns": null}], "writes": [{"table": "field5", "columns": ["calendar", "thefttypeid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO public_schools SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods.products_hourly SELECT * FROM legacy\ncur.execute(\"SELECT price_range, membername FROM fair_wages LIMIT 256\")\n", "labels": {"reads": [{"table": "fair_wages", "columns": ["price_range", "membername"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model artsandcrafts depends on fault_log\ndbt run --select artsandcrafts --vars '{\"source_table\":\"fault_log\"}'\n", "labels": {"reads": [{"table": "fault_log", "columns": null}], "writes": [{"table": "artsandcrafts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table contract_negotiations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"volume\").where(\"dt = current_date()\").writeTo(\"vehicle_prices\").append()\n", "labels": {"reads": [{"table": "volume", "columns": null}], "writes": [{"table": "vehicle_prices", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO policy SELECT count_time, tripid, cb_year FROM wellbeing_programs WHERE count_time > 438\")\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": ["count_time", "tripid", "cb_year"]}], "writes": [{"table": "policy", "columns": ["count_time", "tripid", "cb_year"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 196;\nEOF\n", "labels": {"reads": [{"table": "mission", "columns": ["trainingid", "mining_operation_id"]}], "writes": [{"table": "broadband_providers", "columns": ["trainingid", "mining_operation_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"catalog_structure\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"uel_top10\")\n", "labels": {"reads": [{"table": "catalog_structure", "columns": null}], "writes": [{"table": "uel_top10", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO artists SELECT * FROM legacy\ncur.execute(\"SELECT dates_active, actual_delivery_date FROM ods.shipments_df LIMIT 473\")\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": ["dates_active", "actual_delivery_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"attendee_demographics\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"investments_esg\")\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": null}], "writes": [{"table": "investments_esg", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO disaster_zones SELECT surname, black, crime_type, products_last_year FROM strandings WHERE surname > 286\"\n", "labels": {"reads": [{"table": "strandings", "columns": ["surname", "black", "crime_type", "products_last_year"]}], "writes": [{"table": "disaster_zones", "columns": ["surname", "black", "crime_type", "products_last_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM development_hours\", conn)\ndf.to_sql(\"noise_pollution\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "development_hours", "columns": null}], "writes": [{"table": "noise_pollution", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO shops SELECT days_held, data_usage, galleryname FROM chemicals WHERE days_held > 25\")\n", "labels": {"reads": [{"table": "chemicals", "columns": ["days_held", "data_usage", "galleryname"]}], "writes": [{"table": "shops", "columns": ["days_held", "data_usage", "galleryname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"construction_union\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"tourist_attractions\")\n", "labels": {"reads": [{"table": "construction_union", "columns": null}], "writes": [{"table": "tourist_attractions", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_orders_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"hires\")\n", "labels": {"reads": [{"table": "bi.bi_orders_daily", "columns": null}], "writes": [{"table": "hires", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"policyadvocacyevents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "policyadvocacyevents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO gymnast SELECT a.movieid, b.regionid FROM spacecrafts a JOIN coralreefs b ON a.cropname = b.cropname\"\n", "labels": {"reads": [{"table": "spacecrafts", "columns": null}, {"table": "coralreefs", "columns": null}], "writes": [{"table": "gymnast", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model film_category depends on ai_papers\ndbt build --models film_category --vars '{\"src\":\"ai_papers\"}'\n", "labels": {"reads": [{"table": "ai_papers", "columns": null}], "writes": [{"table": "film_category", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"enrolled_in\")\npush_to_output(df, \"heritage_sites\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "enrolled_in", "columns": null}], "writes": [{"table": "heritage_sites", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO artwork_styles (show_id, research_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "artwork_styles", "columns": ["show_id", "research_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO marine_life_research_stations SELECT instructor_id, policyholder_id, membername FROM organicproducts WHERE instructor_id > 211\"\n", "labels": {"reads": [{"table": "organicproducts", "columns": ["instructor_id", "policyholder_id", "membername"]}], "writes": [{"table": "marine_life_research_stations", "columns": ["instructor_id", "policyholder_id", "membername"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"italy_culture\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"technology_access\")\n", "labels": {"reads": [{"table": "italy_culture", "columns": null}], "writes": [{"table": "technology_access", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO inclusion_efforts SELECT * FROM legacy\nspark.sql(\"INSERT INTO epl_teams SELECT cuisine_name, game_id FROM job_change WHERE cuisine_name > 232\")\n", "labels": {"reads": [{"table": "job_change", "columns": ["cuisine_name", "game_id"]}], "writes": [{"table": "epl_teams", "columns": ["cuisine_name", "game_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO stars SELECT applicant, water_temp, donor_country FROM unesco_intangible_heritage WHERE applicant > 339\");\n", "labels": {"reads": [{"table": "unesco_intangible_heritage", "columns": ["applicant", "water_temp", "donor_country"]}], "writes": [{"table": "stars", "columns": ["applicant", "water_temp", "donor_country"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"gymnast\")\nexport_to_sink(df, \"member_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gymnast", "columns": null}], "writes": [{"table": "member_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"investment_rounds\").where(\"dt = current_date()\").writeTo(\"station_crime_rates\").append()\n", "labels": {"reads": [{"table": "investment_rounds", "columns": null}], "writes": [{"table": "station_crime_rates", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ocean_acidity SELECT * FROM legacy\ncur.execute(\"SELECT movie, name_first FROM baseball_teams LIMIT 276\")\n", "labels": {"reads": [{"table": "baseball_teams", "columns": ["movie", "name_first"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO emergency_categories SELECT recipient_id, assignment_date, materialname, daily_hire_cost FROM apartment_bookings WHERE recipient_id > 227\"\n", "labels": {"reads": [{"table": "apartment_bookings", "columns": ["recipient_id", "assignment_date", "materialname", "daily_hire_cost"]}], "writes": [{"table": "emergency_categories", "columns": ["recipient_id", "assignment_date", "materialname", "daily_hire_cost"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO sfc_articles SELECT player_api_id, inspectionid, outcome_id FROM philadelphia_police_emergencies WHERE player_api_id > 242\"], check=True)\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": ["player_api_id", "inspectionid", "outcome_id"]}], "writes": [{"table": "sfc_articles", "columns": ["player_api_id", "inspectionid", "outcome_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.mart_member_point_hourly SELECT unitprice, eventname FROM cosmetic_formula WHERE unitprice > 477\"\n", "labels": {"reads": [{"table": "cosmetic_formula", "columns": ["unitprice", "eventname"]}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": ["unitprice", "eventname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO authorship (manufacturerid, exit_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "authorship", "columns": ["manufacturerid", "exit_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO hydro_power SELECT * FROM legacy\ncur.execute(\"SELECT bank_name, post_category FROM course_attendance LIMIT 229\")\n", "labels": {"reads": [{"table": "course_attendance", "columns": ["bank_name", "post_category"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"labor_cost\").where(\"dt = current_date()\").writeTo(\"mart.campaigns_full\").append()\n", "labels": {"reads": [{"table": "labor_cost", "columns": null}], "writes": [{"table": "mart.campaigns_full", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO authors SELECT product_stock_number, date_assigned_to, affirmative FROM military_contracts WHERE product_stock_number > 64\"\n", "labels": {"reads": [{"table": "military_contracts", "columns": ["product_stock_number", "date_assigned_to", "affirmative"]}], "writes": [{"table": "authors", "columns": ["product_stock_number", "date_assigned_to", "affirmative"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO garmentproduction SELECT coalquantity, account_number FROM dwd.users_daily WHERE coalquantity > 356\"\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": ["coalquantity", "account_number"]}], "writes": [{"table": "garmentproduction", "columns": ["coalquantity", "account_number"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO recycling_stats SELECT * FROM legacy\ncur.execute(\"SELECT bandmateid, event_location FROM student_program_mapping LIMIT 6\")\n", "labels": {"reads": [{"table": "student_program_mapping", "columns": ["bandmateid", "event_location"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"initiatives_3\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"marathons\")\n", "labels": {"reads": [{"table": "initiatives_3", "columns": null}], "writes": [{"table": "marathons", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO whale_sightings SELECT transaction_type_description, u_id, used_kb FROM ai_safety_papers2 WHERE transaction_type_description > 20\"\n", "labels": {"reads": [{"table": "ai_safety_papers2", "columns": ["transaction_type_description", "u_id", "used_kb"]}], "writes": [{"table": "whale_sightings", "columns": ["transaction_type_description", "u_id", "used_kb"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"city_department\").where(\"dt = current_date()\").writeTo(\"healthcareaccess\").append()\n", "labels": {"reads": [{"table": "city_department", "columns": null}], "writes": [{"table": "healthcareaccess", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO inclusivehousingpolicies SELECT amount, num_solo_exhibitions FROM ads.ads_clicks_delta WHERE amount > 134\"\n", "labels": {"reads": [{"table": "ads.ads_clicks_delta", "columns": ["amount", "num_solo_exhibitions"]}], "writes": [{"table": "inclusivehousingpolicies", "columns": ["amount", "num_solo_exhibitions"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model episodes depends on veteran_stats\ndbt run -s episodes --vars '{\"source_table\":\"veteran_stats\"}'\n", "labels": {"reads": [{"table": "veteran_stats", "columns": null}], "writes": [{"table": "episodes", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ref_product_categories\").toPandas()\ndf[[\"total_donation_amount\", \"cruelty_free\"]].to_sql(\"protein\", engine, index=False)\n", "labels": {"reads": [{"table": "ref_product_categories", "columns": null}], "writes": [{"table": "protein", "columns": ["total_donation_amount", "cruelty_free"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"infra_diversification\")\nsrc.write.insertInto(\"rigs\", overwrite=True)\n", "labels": {"reads": [{"table": "infra_diversification", "columns": null}], "writes": [{"table": "rigs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model security_incidents depends on community_policing_events\ndbt build --models security_incidents --vars '{\"src\":\"community_policing_events\"}'\n", "labels": {"reads": [{"table": "community_policing_events", "columns": null}], "writes": [{"table": "security_incidents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table savings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "savings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table trafficviolations --columns concert_id,rec_engine --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "trafficviolations", "columns": ["concert_id", "rec_engine"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cuisine SELECT researcher_name, policy_number, publication_id, student_id FROM document_functional_areas WHERE researcher_name > 208\"], check=True)\n", "labels": {"reads": [{"table": "document_functional_areas", "columns": ["researcher_name", "policy_number", "publication_id", "student_id"]}], "writes": [{"table": "cuisine", "columns": ["researcher_name", "policy_number", "publication_id", "student_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO crops SELECT item_type, closure_authorised_by_staff_id, policyid, pipeline_name FROM ads.refunds_delta WHERE item_type > 308\")\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": ["item_type", "closure_authorised_by_staff_id", "policyid", "pipeline_name"]}], "writes": [{"table": "crops", "columns": ["item_type", "closure_authorised_by_staff_id", "policyid", "pipeline_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO shipment SELECT count_id, workouttype FROM garmentproduction WHERE count_id > 180\"\n", "labels": {"reads": [{"table": "garmentproduction", "columns": ["count_id", "workouttype"]}], "writes": [{"table": "shipment", "columns": ["count_id", "workouttype"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO unesco_intangible_heritage SELECT excavation_site, lot_details, typeid, login_name FROM procedures WHERE excavation_site > 8\"\n", "labels": {"reads": [{"table": "procedures", "columns": ["excavation_site", "lot_details", "typeid", "login_name"]}], "writes": [{"table": "unesco_intangible_heritage", "columns": ["excavation_site", "lot_details", "typeid", "login_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads_exposure_hourly SELECT mission_name, pallet_id, unit, owner FROM bi.bi_payments WHERE mission_name > 255\"\n", "labels": {"reads": [{"table": "bi.bi_payments", "columns": ["mission_name", "pallet_id", "unit", "owner"]}], "writes": [{"table": "ads_exposure_hourly", "columns": ["mission_name", "pallet_id", "unit", "owner"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 68;\nEOF\n", "labels": {"reads": [{"table": "activity", "columns": ["component_name", "platform", "unit", "communitytype"]}], "writes": [{"table": "dws.risk_score_daily", "columns": ["component_name", "platform", "unit", "communitytype"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"emissions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "emissions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 4;\nEOF\n", "labels": {"reads": [{"table": "orgdonations", "columns": ["resource_type", "visitors", "drug_id"]}], "writes": [{"table": "audience", "columns": ["resource_type", "visitors", "drug_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stg_users_daily\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "stg_users_daily", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ods_products_delta SELECT recruitername, project_education, user_rating FROM dw.dw_coupon_use_daily WHERE recruitername > 80\"], check=True)\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": ["recruitername", "project_education", "user_rating"]}], "writes": [{"table": "ods_products_delta", "columns": ["recruitername", "project_education", "user_rating"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ocean_salinity\", conn)\ndf.to_sql(\"dwd.dwd_products\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": null}], "writes": [{"table": "dwd.dwd_products", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO docking SELECT host_city_id, archaeologistid, brandid, system_id FROM droughthistory WHERE host_city_id > 490\");\n", "labels": {"reads": [{"table": "droughthistory", "columns": ["host_city_id", "archaeologistid", "brandid", "system_id"]}], "writes": [{"table": "docking", "columns": ["host_city_id", "archaeologistid", "brandid", "system_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fairness_scores SELECT safety_record, stars, wellid, vol_id FROM temperature_data WHERE safety_record > 416\"\n", "labels": {"reads": [{"table": "temperature_data", "columns": ["safety_record", "stars", "wellid", "vol_id"]}], "writes": [{"table": "fairness_scores", "columns": ["safety_record", "stars", "wellid", "vol_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"solar_plants\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"workforce_development\")\n", "labels": {"reads": [{"table": "solar_plants", "columns": null}], "writes": [{"table": "workforce_development", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"orgdonations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"london.stations\")\n", "labels": {"reads": [{"table": "orgdonations", "columns": null}], "writes": [{"table": "london.stations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO marine_species_arctic_ocean (check_in_date, running_time) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "marine_species_arctic_ocean", "columns": ["check_in_date", "running_time"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model mental_health_clinics depends on jp_schema.policy_areas\ndbt build --select mental_health_clinics --vars '{\"src\":\"jp_schema.policy_areas\"}'\n", "labels": {"reads": [{"table": "jp_schema.policy_areas", "columns": null}], "writes": [{"table": "mental_health_clinics", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO ads.sessions_hourly SELECT hoursspent, drill_count FROM water_consumption WHERE hoursspent > 132\");\n", "labels": {"reads": [{"table": "water_consumption", "columns": ["hoursspent", "drill_count"]}], "writes": [{"table": "ads.sessions_hourly", "columns": ["hoursspent", "drill_count"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO worker_union SELECT 1\"\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT request_date, home_team_id FROM communication_scores LIMIT 263\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO criminalcases SELECT project_details, value, actual_delivery_date FROM bioprocess_engineering WHERE project_details > 247\")\n", "labels": {"reads": [{"table": "communication_scores", "columns": ["request_date", "home_team_id"]}, {"table": "bioprocess_engineering", "columns": ["project_details", "value", "actual_delivery_date"]}], "writes": [{"table": "criminalcases", "columns": ["project_details", "value", "actual_delivery_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO workercontactinfo SELECT * FROM legacy\nspark.sql(\"INSERT INTO militarypersonnel SELECT roaming_country, advocate_id FROM higher_ed.students WHERE roaming_country > 308\")\n", "labels": {"reads": [{"table": "higher_ed.students", "columns": ["roaming_country", "advocate_id"]}], "writes": [{"table": "militarypersonnel", "columns": ["roaming_country", "advocate_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO climate_data SELECT rating_id, neighborhood FROM buildingpermits WHERE rating_id > 466\"\n", "labels": {"reads": [{"table": "buildingpermits", "columns": ["rating_id", "neighborhood"]}], "writes": [{"table": "climate_data", "columns": ["rating_id", "neighborhood"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dorm\").toPandas()\ndf[[\"violationtype\", \"reservoir_name\"]].to_sql(\"skincareproducts\", engine, index=False)\n", "labels": {"reads": [{"table": "dorm", "columns": null}], "writes": [{"table": "skincareproducts", "columns": ["violationtype", "reservoir_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT researcher, quantity FROM model_data LIMIT 281\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "model_data", "columns": ["researcher", "quantity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO school_details SELECT max_page_size, calendar_date, service_type_code, fouls FROM artworks WHERE max_page_size > 234\");\n", "labels": {"reads": [{"table": "artworks", "columns": ["max_page_size", "calendar_date", "service_type_code", "fouls"]}], "writes": [{"table": "school_details", "columns": ["max_page_size", "calendar_date", "service_type_code", "fouls"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO us_platforms SELECT a.workoutdate, b.labordate FROM diplomacy_events a JOIN philadelphia_police_emergencies b ON a.dance_form = b.dance_form\"\n", "labels": {"reads": [{"table": "diplomacy_events", "columns": null}, {"table": "philadelphia_police_emergencies", "columns": null}], "writes": [{"table": "us_platforms", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO innovation_metrics SELECT a.hotel_chain_name, b.number_of_sightings FROM funding_records a JOIN gamestats b ON a.indigenous = b.indigenous\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "funding_records", "columns": null}, {"table": "gamestats", "columns": null}], "writes": [{"table": "innovation_metrics", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_master_index\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ref_locations\")\n", "labels": {"reads": [{"table": "customer_master_index", "columns": null}], "writes": [{"table": "ref_locations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table mars_rovers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mars_rovers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ocean_pollution\", conn)\ndf.to_sql(\"contract_timeline\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ocean_pollution", "columns": null}], "writes": [{"table": "contract_timeline", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO operate_company SELECT log_entry_description, membership_id, date_of_ceremony FROM carbon_offsets WHERE log_entry_description > 216\"\n", "labels": {"reads": [{"table": "carbon_offsets", "columns": ["log_entry_description", "membership_id", "date_of_ceremony"]}], "writes": [{"table": "operate_company", "columns": ["log_entry_description", "membership_id", "date_of_ceremony"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"packages\").where(\"dt = current_date()\").writeTo(\"co2_sequestration\").append()\n", "labels": {"reads": [{"table": "packages", "columns": null}], "writes": [{"table": "co2_sequestration", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT restaurantname, mission_count FROM galleryc\", engine)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"arrivals\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "galleryc", "columns": ["restaurantname", "mission_count"]}], "writes": [{"table": "arrivals", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 142;\nEOF\n", "labels": {"reads": [{"table": "forests", "columns": ["neighborhoodid", "max_gust_speed_mph", "agreementid"]}], "writes": [{"table": "passenger_trips", "columns": ["neighborhoodid", "max_gust_speed_mph", "agreementid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"donorprograms\")\nsrc.write.insertInto(\"food_items\", overwrite=True)\n", "labels": {"reads": [{"table": "donorprograms", "columns": null}], "writes": [{"table": "food_items", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"restaurant\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "restaurant", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO renewable_projects SELECT * FROM legacy\ncur.execute(\"SELECT i_id, container_id FROM rnd_budget LIMIT 224\")\n", "labels": {"reads": [{"table": "rnd_budget", "columns": ["i_id", "container_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 101;\nEOF\n", "labels": {"reads": [{"table": "mediterranean_salinity", "columns": ["continent_id", "courtid", "railway_id", "playerid"]}], "writes": [{"table": "traditionalarts", "columns": ["continent_id", "courtid", "railway_id", "playerid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"all_star\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "all_star", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws.events\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dws.events", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO decentralized_apps SELECT caseid, nation, stationid FROM articles_es WHERE caseid > 119\")\n", "labels": {"reads": [{"table": "articles_es", "columns": ["caseid", "nation", "stationid"]}], "writes": [{"table": "decentralized_apps", "columns": ["caseid", "nation", "stationid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO offender_demographics SELECT instrument, production_volume, stay FROM journal_committee WHERE instrument > 129\")\n", "labels": {"reads": [{"table": "journal_committee", "columns": ["instrument", "production_volume", "stay"]}], "writes": [{"table": "offender_demographics", "columns": ["instrument", "production_volume", "stay"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"disease_prevalence\").where(\"dt = current_date()\").writeTo(\"donor\").append()\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": null}], "writes": [{"table": "donor", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO asteroids SELECT award_date, investment_date FROM threat_severity WHERE award_date > 151\"\n", "labels": {"reads": [{"table": "threat_severity", "columns": ["award_date", "investment_date"]}], "writes": [{"table": "asteroids", "columns": ["award_date", "investment_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"gamegenres\")\nsrc.write.insertInto(\"marine_life_populations\", overwrite=True)\n", "labels": {"reads": [{"table": "gamegenres", "columns": null}], "writes": [{"table": "marine_life_populations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM smartcities\", conn)\ndf.to_sql(\"dw_users_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "smartcities", "columns": null}], "writes": [{"table": "dw_users_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"document_locations\").where(\"dt = current_date()\").writeTo(\"trains\").append()\n", "labels": {"reads": [{"table": "document_locations", "columns": null}], "writes": [{"table": "trains", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"vrusers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"menu_categories\")\n", "labels": {"reads": [{"table": "vrusers", "columns": null}], "writes": [{"table": "menu_categories", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO ads SELECT program, courtid, mh_id, part_fault_id FROM takes WHERE program > 80\");\n", "labels": {"reads": [{"table": "takes", "columns": ["program", "courtid", "mh_id", "part_fault_id"]}], "writes": [{"table": "ads", "columns": ["program", "courtid", "mh_id", "part_fault_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO energy_production (num_projects, shop_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "energy_production", "columns": ["num_projects", "shop_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO clinic_2022 (incident, theftid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "clinic_2022", "columns": ["incident", "theftid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO excavationsites SELECT * FROM legacy\ncur.execute(\"SELECT customer_id, common_name FROM autonomousvehicleaccidents LIMIT 289\")\n", "labels": {"reads": [{"table": "autonomousvehicleaccidents", "columns": ["customer_id", "common_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO product_ingredient SELECT * FROM legacy\nspark.sql(\"INSERT INTO teacher_development_race SELECT org_name, signupdate, period FROM artifacts WHERE org_name > 118\")\n", "labels": {"reads": [{"table": "artifacts", "columns": ["org_name", "signupdate", "period"]}], "writes": [{"table": "teacher_development_race", "columns": ["org_name", "signupdate", "period"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"menuitems\")\nsrc.write.insertInto(\"city.community_policing\", overwrite=True)\n", "labels": {"reads": [{"table": "menuitems", "columns": null}], "writes": [{"table": "city.community_policing", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO impact_asia SELECT amenity_name, asessment_outcome_code FROM genderdistribution WHERE amenity_name > 488\"], check=True)\n", "labels": {"reads": [{"table": "genderdistribution", "columns": ["amenity_name", "asessment_outcome_code"]}], "writes": [{"table": "impact_asia", "columns": ["amenity_name", "asessment_outcome_code"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO police_stations SELECT store_name, element, scores, low_income_neighborhood FROM human_resources WHERE store_name > 199\")\n", "labels": {"reads": [{"table": "human_resources", "columns": ["store_name", "element", "scores", "low_income_neighborhood"]}], "writes": [{"table": "police_stations", "columns": ["store_name", "element", "scores", "low_income_neighborhood"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.fish_id > 53).all()\n# src table: dw_payments\nengine.execute(\"INSERT INTO subway_stations_seoul SELECT * FROM dw_payments\")\n", "labels": {"reads": [{"table": "dw_payments", "columns": null}], "writes": [{"table": "subway_stations_seoul", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO resource_extraction SELECT part_fault_id, value_points, num_of_audience, status FROM arrivals WHERE part_fault_id > 432\")\n", "labels": {"reads": [{"table": "arrivals", "columns": ["part_fault_id", "value_points", "num_of_audience", "status"]}], "writes": [{"table": "resource_extraction", "columns": ["part_fault_id", "value_points", "num_of_audience", "status"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"web_client_accelerator\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart.campaigns_di\")\n", "labels": {"reads": [{"table": "web_client_accelerator", "columns": null}], "writes": [{"table": "mart.campaigns_di", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO crop_yield SELECT albumname, aid_name, years_working, participation_id FROM conservation WHERE albumname > 172\")\n", "labels": {"reads": [{"table": "conservation", "columns": ["albumname", "aid_name", "years_working", "participation_id"]}], "writes": [{"table": "crop_yield", "columns": ["albumname", "aid_name", "years_working", "participation_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO campaigns SELECT sessionid, attendance_id, worker_name, num_shariah_compliant_investments FROM seafoodsouthafricakenya WHERE sessionid > 177\")\n", "labels": {"reads": [{"table": "seafoodsouthafricakenya", "columns": ["sessionid", "attendance_id", "worker_name", "num_shariah_compliant_investments"]}], "writes": [{"table": "campaigns", "columns": ["sessionid", "attendance_id", "worker_name", "num_shariah_compliant_investments"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"cultural_competency\");\ndf.write().mode(\"overwrite\").saveAsTable(\"feedback\");\n", "labels": {"reads": [{"table": "cultural_competency", "columns": null}], "writes": [{"table": "feedback", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM culturalcompetency\"\n", "labels": {"reads": [{"table": "culturalcompetency", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 116;\nSQL\n", "labels": {"reads": [{"table": "habitats", "columns": ["workout_date", "jul"]}, {"table": "dws.dws_orders", "columns": ["uk_vat_number", "handling_id", "time"]}], "writes": [{"table": "investments", "columns": ["uk_vat_number", "handling_id", "time"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_coupon_use_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"exhibition_record\")\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": null}], "writes": [{"table": "exhibition_record", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO exhibition_artworks SELECT living_wage, university FROM mart.member_point_df WHERE living_wage > 102\");\n", "labels": {"reads": [{"table": "mart.member_point_df", "columns": ["living_wage", "university"]}], "writes": [{"table": "exhibition_artworks", "columns": ["living_wage", "university"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO renewableprojects SELECT incident_type_description, research_id, exploited, division FROM eu_data_usage WHERE incident_type_description > 449\"], check=True)\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": ["incident_type_description", "research_id", "exploited", "division"]}], "writes": [{"table": "renewableprojects", "columns": ["incident_type_description", "research_id", "exploited", "division"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"assignedto\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"maintenance_schedule\")\n", "labels": {"reads": [{"table": "assignedto", "columns": null}], "writes": [{"table": "maintenance_schedule", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.is_public > 216).all()\n# src table: rural.bus_trips\nengine.execute(\"INSERT INTO community_education_programs SELECT * FROM rural.bus_trips\")\n", "labels": {"reads": [{"table": "rural.bus_trips", "columns": null}], "writes": [{"table": "community_education_programs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mental_health_clinics SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"renewableprojects\").where(\"dt = current_date()\").writeTo(\"trainings\").append()\n", "labels": {"reads": [{"table": "renewableprojects", "columns": null}], "writes": [{"table": "trainings", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"esportsteamsafrica\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"wastegeneration\")\n", "labels": {"reads": [{"table": "esportsteamsafrica", "columns": null}], "writes": [{"table": "wastegeneration", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO military_aircraft_maintenance SELECT mediatorid, initiative_type, invested FROM eco_diversification_investment WHERE mediatorid > 188\"\n", "labels": {"reads": [{"table": "eco_diversification_investment", "columns": ["mediatorid", "initiative_type", "invested"]}], "writes": [{"table": "military_aircraft_maintenance", "columns": ["mediatorid", "initiative_type", "invested"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM concert_sales\", conn)\ndf.to_sql(\"waste\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "concert_sales", "columns": null}], "writes": [{"table": "waste", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO circular_economy_initiatives SELECT financially_capable, copy_number, contract_type, eco_friendly FROM ocean_acidification WHERE financially_capable > 428\"], check=True)\n", "labels": {"reads": [{"table": "ocean_acidification", "columns": ["financially_capable", "copy_number", "contract_type", "eco_friendly"]}], "writes": [{"table": "circular_economy_initiatives", "columns": ["financially_capable", "copy_number", "contract_type", "eco_friendly"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO stg_orders_hourly SELECT * FROM legacy\nspark.sql(\"INSERT INTO vehicle_registrations SELECT resname, customer_phone, project_date, carbon_footprint FROM port_office WHERE resname > 420\")\n", "labels": {"reads": [{"table": "port_office", "columns": ["resname", "customer_phone", "project_date", "carbon_footprint"]}], "writes": [{"table": "vehicle_registrations", "columns": ["resname", "customer_phone", "project_date", "carbon_footprint"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO invoice_lines (community_members, mission_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "invoice_lines", "columns": ["community_members", "mission_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 243;\nEOF\n", "labels": {"reads": [{"table": "fault_log_parts", "columns": ["party_email", "low_estimate", "date_of_ceremony", "household_size"]}], "writes": [{"table": "mart.mart_refunds_di", "columns": ["party_email", "low_estimate", "date_of_ceremony", "household_size"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM appliances\", conn)\ndf.to_sql(\"watertreatmentplants\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "appliances", "columns": null}], "writes": [{"table": "watertreatmentplants", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO maintenance_engineers SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"retailerg\")\nsrc.write.insertInto(\"investment\", overwrite=True)\n", "labels": {"reads": [{"table": "retailerg", "columns": null}], "writes": [{"table": "investment", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.stg_risk_score SELECT a.card_number, b.healthequitymetricscore FROM conditions a JOIN fans b ON a.attribute_data_type = b.attribute_data_type\"\n", "labels": {"reads": [{"table": "conditions", "columns": null}, {"table": "fans", "columns": null}], "writes": [{"table": "stg.stg_risk_score", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO company SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO student_access SELECT * FROM legacy\ncur.execute(\"SELECT cargoid, shale_play FROM mart.shipments_delta LIMIT 201\")\n", "labels": {"reads": [{"table": "mart.shipments_delta", "columns": ["cargoid", "shale_play"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO shark_biomass SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"solar_farms\").toPandas()\ndf[[\"player\", \"machine\"]].to_sql(\"mental_health_center\", engine, index=False)\n", "labels": {"reads": [{"table": "solar_farms", "columns": null}], "writes": [{"table": "mental_health_center", "columns": ["player", "machine"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"apartment_bookings\").toPandas()\ndf[[\"donation_year\", \"minename\"]].to_sql(\"defenseprojects\", engine, index=False)\n", "labels": {"reads": [{"table": "apartment_bookings", "columns": null}], "writes": [{"table": "defenseprojects", "columns": ["donation_year", "minename"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"marine_life_data\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "marine_life_data", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO climate_projects SELECT sitename, building_address FROM birds WHERE sitename > 3\"\n", "labels": {"reads": [{"table": "birds", "columns": ["sitename", "building_address"]}], "writes": [{"table": "climate_projects", "columns": ["sitename", "building_address"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO disaster_response SELECT open_year, organic_ingredients_percentage FROM mediators WHERE open_year > 462\"], check=True)\n", "labels": {"reads": [{"table": "mediators", "columns": ["open_year", "organic_ingredients_percentage"]}], "writes": [{"table": "disaster_response", "columns": ["open_year", "organic_ingredients_percentage"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO production_sites SELECT claimtype, plant_name, acc_percent FROM vehicle_data WHERE claimtype > 135\")\n", "labels": {"reads": [{"table": "vehicle_data", "columns": ["claimtype", "plant_name", "acc_percent"]}], "writes": [{"table": "production_sites", "columns": ["claimtype", "plant_name", "acc_percent"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM humanitarian_assistance\", conn)\ndf.to_sql(\"uel_top10\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "humanitarian_assistance", "columns": null}], "writes": [{"table": "uel_top10", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT stream_id, cargo_id FROM military_personnel_africa LIMIT 314\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "military_personnel_africa", "columns": ["stream_id", "cargo_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO energy_prices SELECT playtime, origin_city, date_incident_end FROM customer_size_diversity WHERE playtime > 338\");\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": ["playtime", "origin_city", "date_incident_end"]}], "writes": [{"table": "energy_prices", "columns": ["playtime", "origin_city", "date_incident_end"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table monthly_temp --columns number_of_hosts,grade --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "monthly_temp", "columns": ["number_of_hosts", "grade"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"humanitarian_operations\");\ndf.write().mode(\"overwrite\").saveAsTable(\"shelters\");\n", "labels": {"reads": [{"table": "humanitarian_operations", "columns": null}], "writes": [{"table": "shelters", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM user_ad_interactions\", conn)\ndf.to_sql(\"injury_accident\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": null}], "writes": [{"table": "injury_accident", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO dali SELECT person_id, diplomacy_id, has_spf FROM player_sessions WHERE person_id > 427\");\n", "labels": {"reads": [{"table": "player_sessions", "columns": ["person_id", "diplomacy_id", "has_spf"]}], "writes": [{"table": "dali", "columns": ["person_id", "diplomacy_id", "has_spf"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO camera_lens SELECT opening_year, incident_id FROM customers_cards WHERE opening_year > 440\"], check=True)\n", "labels": {"reads": [{"table": "customers_cards", "columns": ["opening_year", "incident_id"]}], "writes": [{"table": "camera_lens", "columns": ["opening_year", "incident_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO mars_missions SELECT * FROM legacy\nspark.sql(\"INSERT INTO waterusage SELECT local_authority, incident_type, fleet_id FROM oceanography WHERE local_authority > 195\")\n", "labels": {"reads": [{"table": "oceanography", "columns": ["local_authority", "incident_type", "fleet_id"]}], "writes": [{"table": "waterusage", "columns": ["local_authority", "incident_type", "fleet_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"agriculturalinvestments\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "agriculturalinvestments", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO renewableprojects SELECT a.donationdate, b.carbon_footprint FROM miningdepartment a JOIN teams_mascots b ON a.measurement_id = b.measurement_id\"\n", "labels": {"reads": [{"table": "miningdepartment", "columns": null}, {"table": "teams_mascots", "columns": null}], "writes": [{"table": "renewableprojects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO collectivebargaining SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"student_program_mapping\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "student_program_mapping", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO pharmasales SELECT pet_age, evaluated_for_fairness FROM tourism_activities WHERE pet_age > 485\"\n", "labels": {"reads": [{"table": "tourism_activities", "columns": ["pet_age", "evaluated_for_fairness"]}], "writes": [{"table": "pharmasales", "columns": ["pet_age", "evaluated_for_fairness"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO bi.bi_campaigns_delta SELECT founder_veteran, official_native_language, building_manager, routeid FROM sanctuaryanimals WHERE founder_veteran > 284\")\n", "labels": {"reads": [{"table": "sanctuaryanimals", "columns": ["founder_veteran", "official_native_language", "building_manager", "routeid"]}], "writes": [{"table": "bi.bi_campaigns_delta", "columns": ["founder_veteran", "official_native_language", "building_manager", "routeid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO genderdistribution (membership_type, hoursspent) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "genderdistribution", "columns": ["membership_type", "hoursspent"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO fish_purchases SELECT * FROM legacy\nspark.sql(\"INSERT INTO socially_responsible_loans SELECT life_expectancy, model_id, date_complaint_raised FROM stg.stg_coupon_use_hourly WHERE life_expectancy > 472\")\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_hourly", "columns": ["life_expectancy", "model_id", "date_complaint_raised"]}], "writes": [{"table": "socially_responsible_loans", "columns": ["life_expectancy", "model_id", "date_complaint_raised"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO humanitarian_aid SELECT cropname, daily_co2_emission, business_size FROM marine_species_indian WHERE cropname > 349\")\n", "labels": {"reads": [{"table": "marine_species_indian", "columns": ["cropname", "daily_co2_emission", "business_size"]}], "writes": [{"table": "humanitarian_aid", "columns": ["cropname", "daily_co2_emission", "business_size"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dws.shipments_daily SELECT a.comments, b.shipped_to FROM drivers a JOIN research_grants b ON a.ship_date = b.ship_date\"\n", "labels": {"reads": [{"table": "drivers", "columns": null}, {"table": "research_grants", "columns": null}], "writes": [{"table": "dws.shipments_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM counties\"\n", "labels": {"reads": [{"table": "counties", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"london.stations\").toPandas()\ndf[[\"district_id\", \"hardware_colours\"]].to_sql(\"ads.ads_refunds_hourly\", engine, index=False)\n", "labels": {"reads": [{"table": "london.stations", "columns": null}], "writes": [{"table": "ads.ads_refunds_hourly", "columns": ["district_id", "hardware_colours"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"daily_revenue\")\nsrc.write.insertInto(\"donations2022\", overwrite=True)\n", "labels": {"reads": [{"table": "daily_revenue", "columns": null}], "writes": [{"table": "donations2022", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO recycled_polyester SELECT sustainability_rating, material, served_subscribers, ingredient FROM time_dim WHERE sustainability_rating > 264\");\n", "labels": {"reads": [{"table": "time_dim", "columns": ["sustainability_rating", "material", "served_subscribers", "ingredient"]}], "writes": [{"table": "recycled_polyester", "columns": ["sustainability_rating", "material", "served_subscribers", "ingredient"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO mars_missions SELECT threat, code, trade_name FROM activities WHERE threat > 411\");\n", "labels": {"reads": [{"table": "activities", "columns": ["threat", "code", "trade_name"]}], "writes": [{"table": "mars_missions", "columns": ["threat", "code", "trade_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO community_development.transactions SELECT art_id, customer_event_id, share_count, order_shipping_charges FROM video_games WHERE art_id > 246\"\n", "labels": {"reads": [{"table": "video_games", "columns": ["art_id", "customer_event_id", "share_count", "order_shipping_charges"]}], "writes": [{"table": "community_development.transactions", "columns": ["art_id", "customer_event_id", "share_count", "order_shipping_charges"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO platform SELECT billingamount, vesselid, fanid FROM takes WHERE billingamount > 359\"], check=True)\n", "labels": {"reads": [{"table": "takes", "columns": ["billingamount", "vesselid", "fanid"]}], "writes": [{"table": "platform", "columns": ["billingamount", "vesselid", "fanid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT produceid, directed_by FROM euro_champs_track_field LIMIT 310\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO agencies SELECT venue, vendor_name, mean_visibility_miles FROM renewable_energy_investments WHERE venue > 67\")\n", "labels": {"reads": [{"table": "euro_champs_track_field", "columns": ["produceid", "directed_by"]}, {"table": "renewable_energy_investments", "columns": ["venue", "vendor_name", "mean_visibility_miles"]}], "writes": [{"table": "agencies", "columns": ["venue", "vendor_name", "mean_visibility_miles"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation_trips\").toPandas()\ndf[[\"phone\", \"host\"]].to_sql(\"department_stores\", engine, index=False)\n", "labels": {"reads": [{"table": "transportation_trips", "columns": null}], "writes": [{"table": "department_stores", "columns": ["phone", "host"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO timed_locations_of_things SELECT a.incident_region, b.primary_conference FROM co2_sequestration a JOIN accelerator_compatible_browser b ON a.formats = b.formats\"\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": null}, {"table": "accelerator_compatible_browser", "columns": null}], "writes": [{"table": "timed_locations_of_things", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nsql = \"INSERT INTO farm_competition SELECT a.curator, b.address_line_1 FROM high_risk a JOIN shared_rides_tokyo b ON a.device_name = b.device_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "high_risk", "columns": null}, {"table": "shared_rides_tokyo", "columns": null}], "writes": [{"table": "farm_competition", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO defense_diplomacy SELECT years_working, attendeename, airport FROM culturalcompetencytrainings WHERE years_working > 160\");\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": ["years_working", "attendeename", "airport"]}], "writes": [{"table": "defense_diplomacy", "columns": ["years_working", "attendeename", "airport"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"medicine\").where(\"dt = current_date()\").writeTo(\"contract_negotiations_un\").append()\n", "labels": {"reads": [{"table": "medicine", "columns": null}], "writes": [{"table": "contract_negotiations_un", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM legal_aid_organizations\"\n", "labels": {"reads": [{"table": "legal_aid_organizations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO autonomousvehicleaccidents SELECT shipped_to, taskdate, assessmentdate FROM request WHERE shipped_to > 206\"\n", "labels": {"reads": [{"table": "request", "columns": ["shipped_to", "taskdate", "assessmentdate"]}], "writes": [{"table": "autonomousvehicleaccidents", "columns": ["shipped_to", "taskdate", "assessmentdate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rigs\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"flights\")\n", "labels": {"reads": [{"table": "rigs", "columns": null}], "writes": [{"table": "flights", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM debris\"\n", "labels": {"reads": [{"table": "debris", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table wind_turbines --columns lastname,openingid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "wind_turbines", "columns": ["lastname", "openingid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"financial_capability_id\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "financial_capability_id", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO shariah_compliant_loans SELECT trip_type, enrollment_date FROM drama_workshop_groups WHERE trip_type > 390\"\n", "labels": {"reads": [{"table": "drama_workshop_groups", "columns": ["trip_type", "enrollment_date"]}], "writes": [{"table": "shariah_compliant_loans", "columns": ["trip_type", "enrollment_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.risk_score_hourly SELECT a.max_temperature_f, b.inspection_id FROM dws.dws_coupon_use_di a JOIN music_events b ON a.number_of_platforms = b.number_of_platforms\"\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_di", "columns": null}, {"table": "music_events", "columns": null}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"airportdata\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"clinics\")\n", "labels": {"reads": [{"table": "airportdata", "columns": null}], "writes": [{"table": "clinics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"classicgame\");\ndf.write().mode(\"overwrite\").saveAsTable(\"developers\");\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": [{"table": "developers", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO categories SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO labor_unions SELECT diet, quality_rank, color_code, yearid FROM workforce_development WHERE diet > 343\");\n", "labels": {"reads": [{"table": "workforce_development", "columns": ["diet", "quality_rank", "color_code", "yearid"]}], "writes": [{"table": "labor_unions", "columns": ["diet", "quality_rank", "color_code", "yearid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO species_observations SELECT destinationid, menuname, menuid FROM middle_east_military_spending WHERE destinationid > 154\");\n", "labels": {"reads": [{"table": "middle_east_military_spending", "columns": ["destinationid", "menuname", "menuid"]}], "writes": [{"table": "species_observations", "columns": ["destinationid", "menuname", "menuid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mart.mart_vendors\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "mart.mart_vendors", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO disaster_response_donations SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"funding_records\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "funding_records", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO soil_moisture SELECT total_horses, projectid, browser_id FROM renewable_projects WHERE total_horses > 182\"\n", "labels": {"reads": [{"table": "renewable_projects", "columns": ["total_horses", "projectid", "browser_id"]}], "writes": [{"table": "soil_moisture", "columns": ["total_horses", "projectid", "browser_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO economic_diversification_efforts SELECT * FROM legacy\nspark.sql(\"INSERT INTO player_stats SELECT is_electric, feb FROM algorithmic_fairness_incidents WHERE is_electric > 499\")\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents", "columns": ["is_electric", "feb"]}], "writes": [{"table": "player_stats", "columns": ["is_electric", "feb"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT production, fault_short_name FROM chemical_concentration LIMIT 161\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO enrolled_in SELECT launch_agency, claimamount, wrestler_id FROM algorithmic_fairness_incidents WHERE launch_agency > 303\")\n", "labels": {"reads": [{"table": "chemical_concentration", "columns": ["production", "fault_short_name"]}, {"table": "algorithmic_fairness_incidents", "columns": ["launch_agency", "claimamount", "wrestler_id"]}], "writes": [{"table": "enrolled_in", "columns": ["launch_agency", "claimamount", "wrestler_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO stg.stg_coupon_use_di SELECT catalog_entry_name, issue_date, humidity, document_status_code FROM european_healthcare WHERE catalog_entry_name > 293\")\n", "labels": {"reads": [{"table": "european_healthcare", "columns": ["catalog_entry_name", "issue_date", "humidity", "document_status_code"]}], "writes": [{"table": "stg.stg_coupon_use_di", "columns": ["catalog_entry_name", "issue_date", "humidity", "document_status_code"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table student_lifelong_learning --target-dir /tmp/land\n", "labels": {"reads": [{"table": "student_lifelong_learning", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"settlements\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"classroom\")\n", "labels": {"reads": [{"table": "settlements", "columns": null}], "writes": [{"table": "classroom", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"people\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"crops_year\")\n", "labels": {"reads": [{"table": "people", "columns": null}], "writes": [{"table": "crops_year", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model publications depends on vrplayers\ndbt run --select publications --vars '{\"source_table\":\"vrplayers\"}'\n", "labels": {"reads": [{"table": "vrplayers", "columns": null}], "writes": [{"table": "publications", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO staff SELECT eventid, garment_type FROM bi.bi_risk_score_delta WHERE eventid > 150\"\n", "labels": {"reads": [{"table": "bi.bi_risk_score_delta", "columns": ["eventid", "garment_type"]}], "writes": [{"table": "staff", "columns": ["eventid", "garment_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mental_health_clinics\").where(\"dt = current_date()\").writeTo(\"healthcare_system\").append()\n", "labels": {"reads": [{"table": "mental_health_clinics", "columns": null}], "writes": [{"table": "healthcare_system", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 370;\nEOF\n", "labels": {"reads": [{"table": "waterconservation", "columns": ["agegroup", "sportname", "mediatypeid"]}], "writes": [{"table": "heritage_sites_3", "columns": ["agegroup", "sportname", "mediatypeid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO instructor SELECT a.quantitysold, b.maintenance_id FROM eventlocations a JOIN language b ON a.cultural_significance = b.cultural_significance\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "eventlocations", "columns": null}, {"table": "language", "columns": null}], "writes": [{"table": "instructor", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicle_safety_testing\").toPandas()\ndf[[\"membership_id\", \"campusfee\"]].to_sql(\"fieldd_info\", engine, index=False)\n", "labels": {"reads": [{"table": "vehicle_safety_testing", "columns": null}], "writes": [{"table": "fieldd_info", "columns": ["membership_id", "campusfee"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_table(ctx, \"food_justice_contributors\")\nwrite_to_store(df, \"membership_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "food_justice_contributors", "columns": null}], "writes": [{"table": "membership_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO military_equipment SELECT * FROM legacy\ncur.execute(\"SELECT hub_id, company_name FROM customer_events LIMIT 305\")\n", "labels": {"reads": [{"table": "customer_events", "columns": ["hub_id", "company_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"protein\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "protein", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"startup_founders\");\ndf.write().mode(\"overwrite\").saveAsTable(\"sustainablebrands\");\n", "labels": {"reads": [{"table": "startup_founders", "columns": null}], "writes": [{"table": "sustainablebrands", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO film_market_estimation SELECT a.purchaseid, b.firstname FROM certificate a JOIN veterans b ON a.premise_id = b.premise_id\"\n", "labels": {"reads": [{"table": "certificate", "columns": null}, {"table": "veterans", "columns": null}], "writes": [{"table": "film_market_estimation", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dailyapplestreams\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"phone\")\n", "labels": {"reads": [{"table": "dailyapplestreams", "columns": null}], "writes": [{"table": "phone", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model humanitarian_assistance depends on water_consumption\ndbt run --models humanitarian_assistance --vars '{\"src\":\"water_consumption\"}'\n", "labels": {"reads": [{"table": "water_consumption", "columns": null}], "writes": [{"table": "humanitarian_assistance", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"arcticocean\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"surveylocations\")\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}], "writes": [{"table": "surveylocations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"latam_schema.education_budget\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"music_streaming\")\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": null}], "writes": [{"table": "music_streaming", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO climate_finance_organizations SELECT a.unit_name, b.judge_state FROM vessel_incident_count a JOIN tokyo_water_consumption b ON a.isfirstattendee = b.isfirstattendee\"\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": null}, {"table": "tokyo_water_consumption", "columns": null}], "writes": [{"table": "climate_finance_organizations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dws.coupon_use_di\");\ndf.write().mode(\"overwrite\").saveAsTable(\"high_risk\");\n", "labels": {"reads": [{"table": "dws.coupon_use_di", "columns": null}], "writes": [{"table": "high_risk", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO artworksales SELECT a.patientid, b.opening_hours FROM cultivators a JOIN test_drives b ON a.quantity = b.quantity\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "cultivators", "columns": null}, {"table": "test_drives", "columns": null}], "writes": [{"table": "artworksales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"discipline_enrollments\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "discipline_enrollments", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hiv > 5).all()\n# src table: total_capacity\nengine.execute(\"INSERT INTO cloud_issues SELECT * FROM total_capacity\")\n", "labels": {"reads": [{"table": "total_capacity", "columns": null}], "writes": [{"table": "cloud_issues", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO artifacts SELECT nation, continent_id, physical FROM country_sustainable_chains WHERE nation > 274\"\n", "labels": {"reads": [{"table": "country_sustainable_chains", "columns": ["nation", "continent_id", "physical"]}], "writes": [{"table": "artifacts", "columns": ["nation", "continent_id", "physical"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO packages SELECT unionname, ad_id FROM esports_teams WHERE unionname > 284\"\n", "labels": {"reads": [{"table": "esports_teams", "columns": ["unionname", "ad_id"]}], "writes": [{"table": "packages", "columns": ["unionname", "ad_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT asset_disposed_date, grantid FROM submarine_canyons LIMIT 167\")\nimport logging\nspark.sql(\"INSERT INTO urban_farms SELECT cname, class_section, installed_date FROM ods_exposure_delta WHERE cname > 90\")\n", "labels": {"reads": [{"table": "submarine_canyons", "columns": ["asset_disposed_date", "grantid"]}, {"table": "ods_exposure_delta", "columns": ["cname", "class_section", "installed_date"]}], "writes": [{"table": "urban_farms", "columns": ["cname", "class_section", "installed_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"visits\");\ndf.write().mode(\"overwrite\").saveAsTable(\"port_office\");\n", "labels": {"reads": [{"table": "visits", "columns": null}], "writes": [{"table": "port_office", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO investors SELECT product_type_code, stat_id FROM safetytesting WHERE product_type_code > 462\"\n", "labels": {"reads": [{"table": "safetytesting", "columns": ["product_type_code", "stat_id"]}], "writes": [{"table": "investors", "columns": ["product_type_code", "stat_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"strandings\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "strandings", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO teachers SELECT a.amount_claimed, b.customerid FROM uniteddefense.equipmentsales a JOIN gamereviews b ON a.framework = b.framework\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "uniteddefense.equipmentsales", "columns": null}, {"table": "gamereviews", "columns": null}], "writes": [{"table": "teachers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"coal\")\npersist_to_warehouse(df, \"bi.bi_inventory_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "coal", "columns": null}], "writes": [{"table": "bi.bi_inventory_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ods.ods_clicks_di\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ods.ods_users_daily\");\n", "labels": {"reads": [{"table": "ods.ods_clicks_di", "columns": null}], "writes": [{"table": "ods.ods_users_daily", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT stu_hrs, flag FROM mart.mart_refunds_di\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"nutritionfacts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_refunds_di", "columns": ["stu_hrs", "flag"]}], "writes": [{"table": "nutritionfacts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO sales_quarterly SELECT has_aloe_vera, stat_type, sales_transaction_id, product_stock_number FROM shipmentinfo WHERE has_aloe_vera > 5\")\n", "labels": {"reads": [{"table": "shipmentinfo", "columns": ["has_aloe_vera", "stat_type", "sales_transaction_id", "product_stock_number"]}], "writes": [{"table": "sales_quarterly", "columns": ["has_aloe_vera", "stat_type", "sales_transaction_id", "product_stock_number"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO player_coach SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.application > 124).all()\n# src table: dwd.products_hourly\nengine.execute(\"INSERT INTO properties SELECT * FROM dwd.products_hourly\")\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": null}], "writes": [{"table": "properties", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"co2_emission_reduction\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "co2_emission_reduction", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"maintenance_schedule\").toPandas()\ndf[[\"event_type\", \"programarea\"]].to_sql(\"mobile_customers_global\", engine, index=False)\n", "labels": {"reads": [{"table": "maintenance_schedule", "columns": null}], "writes": [{"table": "mobile_customers_global", "columns": ["event_type", "programarea"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"production_sites\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ocean_health_monitor\")\n", "labels": {"reads": [{"table": "production_sites", "columns": null}], "writes": [{"table": "ocean_health_monitor", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 459;\nSQL\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": ["artifactname", "domestic_passengers"]}, {"table": "demographics", "columns": ["therapy_type", "vaccinations"]}], "writes": [{"table": "otas", "columns": ["therapy_type", "vaccinations"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 345;\nEOF\n", "labels": {"reads": [{"table": "contracts", "columns": ["departure_date", "fault_short_name", "part_fault_id", "hiv"]}], "writes": [{"table": "menu_vendors", "columns": ["departure_date", "fault_short_name", "part_fault_id", "hiv"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"race_ethnicity\")\nsrc.write.insertInto(\"visualartprograms\", overwrite=True)\n", "labels": {"reads": [{"table": "race_ethnicity", "columns": null}], "writes": [{"table": "visualartprograms", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ods.ods_campaigns_hourly\", conn)\ndf.to_sql(\"driver\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_hourly", "columns": null}], "writes": [{"table": "driver", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"provinces\").toPandas()\ndf[[\"is_ev\", \"donation_date\"]].to_sql(\"container_receipts\", engine, index=False)\n", "labels": {"reads": [{"table": "provinces", "columns": null}], "writes": [{"table": "container_receipts", "columns": ["is_ev", "donation_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stories\");\ndf.write().mode(\"overwrite\").saveAsTable(\"waste_generation_metrics\");\n", "labels": {"reads": [{"table": "stories", "columns": null}], "writes": [{"table": "waste_generation_metrics", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"genetic.projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"climate_investments\")\n", "labels": {"reads": [{"table": "genetic.projects", "columns": null}], "writes": [{"table": "climate_investments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT grant_amount, likes FROM students_lifelong_learning LIMIT 94\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO department_publications SELECT number_of_vessels, coownerid FROM property_community WHERE number_of_vessels > 476\")\n", "labels": {"reads": [{"table": "students_lifelong_learning", "columns": ["grant_amount", "likes"]}, {"table": "property_community", "columns": ["number_of_vessels", "coownerid"]}], "writes": [{"table": "department_publications", "columns": ["number_of_vessels", "coownerid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"energy_efficiency_projects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "energy_efficiency_projects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO site SELECT account_name, operationname, subject_id FROM artifactanalysis WHERE account_name > 478\"\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": ["account_name", "operationname", "subject_id"]}], "writes": [{"table": "site", "columns": ["account_name", "operationname", "subject_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT role_code, company FROM dws.dws_member_point_df LIMIT 356\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": ["role_code", "company"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws.cart_item_full\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table performers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "performers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bridgerainfall\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ap_budget\")\n", "labels": {"reads": [{"table": "bridgerainfall", "columns": null}], "writes": [{"table": "ap_budget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 319;\nEOF\n", "labels": {"reads": [{"table": "mineral_extraction", "columns": ["raceid", "draft_pick_number", "billing_country", "launch"]}], "writes": [{"table": "socialimpactinvestments", "columns": ["raceid", "draft_pick_number", "billing_country", "launch"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO salinity_readings SELECT marketing_region_code, international_passengers, line_name FROM user_ad_interactions WHERE marketing_region_code > 163\");\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": ["marketing_region_code", "international_passengers", "line_name"]}], "writes": [{"table": "salinity_readings", "columns": ["marketing_region_code", "international_passengers", "line_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO warehouses SELECT program_name, hours_played, active FROM water_distribution WHERE program_name > 474\"], check=True)\n", "labels": {"reads": [{"table": "water_distribution", "columns": ["program_name", "hours_played", "active"]}], "writes": [{"table": "warehouses", "columns": ["program_name", "hours_played", "active"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_initiatives\").toPandas()\ndf[[\"furniture_id\", \"master_customer_id\"]].to_sql(\"ods.clicks_delta\", engine, index=False)\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": null}], "writes": [{"table": "ods.clicks_delta", "columns": ["furniture_id", "master_customer_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 201;\nEOF\n", "labels": {"reads": [{"table": "salary", "columns": ["excavation_site", "customer_number", "workoutdate"]}], "writes": [{"table": "asteroids", "columns": ["excavation_site", "customer_number", "workoutdate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nsql = \"INSERT INTO traffic SELECT a.sales_transaction_id, b.competition_type FROM eco_hotels a JOIN intelligence_agency b ON a.race = b.race\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "eco_hotels", "columns": null}, {"table": "intelligence_agency", "columns": null}], "writes": [{"table": "traffic", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"lead_mines\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"green_energy_lending_programs\")\n", "labels": {"reads": [{"table": "lead_mines", "columns": null}], "writes": [{"table": "green_energy_lending_programs", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO network_infrastructure SELECT a.mission_category, b.sale_amount FROM workplaces a JOIN course b ON a.make = b.make\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "workplaces", "columns": null}, {"table": "course", "columns": null}], "writes": [{"table": "network_infrastructure", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO satellite_missions_large SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table marine_species_status --target-dir /tmp/land\n", "labels": {"reads": [{"table": "marine_species_status", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"aus_wellbeing\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"food_justice_contributors\")\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": null}], "writes": [{"table": "food_justice_contributors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO funding_rounds SELECT a.material_date, b.oct FROM country_sustainable_chains a JOIN stations b ON a.contract_type = b.contract_type\"\n", "labels": {"reads": [{"table": "country_sustainable_chains", "columns": null}, {"table": "stations", "columns": null}], "writes": [{"table": "funding_rounds", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model recycling_centers depends on investment_rounds\ndbt run --select recycling_centers --vars '{\"src\":\"investment_rounds\"}'\n", "labels": {"reads": [{"table": "investment_rounds", "columns": null}], "writes": [{"table": "recycling_centers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO charging_stations SELECT a.provider_parity_score, b.production_volume FROM artist_demographics a JOIN safetytests b ON a.attack_count = b.attack_count\"\n", "labels": {"reads": [{"table": "artist_demographics", "columns": null}, {"table": "safetytests", "columns": null}], "writes": [{"table": "charging_stations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO engineer_skills SELECT course_id, severity, engagement_date FROM asteroids WHERE course_id > 437\")\n", "labels": {"reads": [{"table": "asteroids", "columns": ["course_id", "severity", "engagement_date"]}], "writes": [{"table": "engineer_skills", "columns": ["course_id", "severity", "engagement_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"crimes\")\nsrc.write.insertInto(\"bi.bi_events_full\", overwrite=True)\n", "labels": {"reads": [{"table": "crimes", "columns": null}], "writes": [{"table": "bi.bi_events_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO inclusivehousing.affordablehousing (amenid, shipment_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "inclusivehousing.affordablehousing", "columns": ["amenid", "shipment_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"restaurant_revenue\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"defenseprojects\")\ndump_to_output(df, \"mart.mart_member_point_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "defenseprojects", "columns": null}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.team_id_br > 257).all()\n# src table: smartcityprojects\nengine.execute(\"INSERT INTO pediatricians SELECT * FROM smartcityprojects\")\n", "labels": {"reads": [{"table": "smartcityprojects", "columns": null}], "writes": [{"table": "pediatricians", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO satelliteimagery SELECT ai_powered_features, signupdate, numcases, date_test_taken FROM attorney_billing_rates WHERE ai_powered_features > 131\"\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": ["ai_powered_features", "signupdate", "numcases", "date_test_taken"]}], "writes": [{"table": "satelliteimagery", "columns": ["ai_powered_features", "signupdate", "numcases", "date_test_taken"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"biomes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"policyanalysis\")\n", "labels": {"reads": [{"table": "biomes", "columns": null}], "writes": [{"table": "policyanalysis", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"singer_in_concert\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"startup_founders\")\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": [{"table": "startup_founders", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO flights SELECT workshop_name, researcher_name, year_founded, order_item_status FROM dws.dws_refunds_hourly WHERE workshop_name > 279\"], check=True)\n", "labels": {"reads": [{"table": "dws.dws_refunds_hourly", "columns": ["workshop_name", "researcher_name", "year_founded", "order_item_status"]}], "writes": [{"table": "flights", "columns": ["workshop_name", "researcher_name", "year_founded", "order_item_status"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table oceanography --target-dir /tmp/land\n", "labels": {"reads": [{"table": "oceanography", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO rural_hospitals SELECT a.refugee_name, b.organic_matter FROM prepaid_mobile a JOIN yearly_production b ON a.quality_rank = b.quality_rank\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "prepaid_mobile", "columns": null}, {"table": "yearly_production", "columns": null}], "writes": [{"table": "rural_hospitals", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO legal_aid_providers (volunteername, wildlife_type_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "legal_aid_providers", "columns": ["volunteername", "wildlife_type_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO train (account_details, vessel_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "train", "columns": ["account_details", "vessel_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO agricultural_innovation SELECT street_address, winning_pilot FROM cultivators WHERE street_address > 331\"\n", "labels": {"reads": [{"table": "cultivators", "columns": ["street_address", "winning_pilot"]}], "writes": [{"table": "agricultural_innovation", "columns": ["street_address", "winning_pilot"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table esportsevents --columns total_investment,fault_short_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "esportsevents", "columns": ["total_investment", "fault_short_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO building_permits SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO department SELECT projectname, eia_date, genrename, member_name FROM electricvehiclestats WHERE projectname > 97\")\n", "labels": {"reads": [{"table": "electricvehiclestats", "columns": ["projectname", "eia_date", "genrename", "member_name"]}], "writes": [{"table": "department", "columns": ["projectname", "eia_date", "genrename", "member_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"infrastructure\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "infrastructure", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT uid, video_id FROM concentrateprices LIMIT 50\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "concentrateprices", "columns": ["uid", "video_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"worker_scores\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ads.orders_daily\");\n", "labels": {"reads": [{"table": "worker_scores", "columns": null}], "writes": [{"table": "ads.orders_daily", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.end_date > 387).all()\n# src table: labor_statistics\nengine.execute(\"INSERT INTO healthcare_access_v2 SELECT * FROM labor_statistics\")\n", "labels": {"reads": [{"table": "labor_statistics", "columns": null}], "writes": [{"table": "healthcare_access_v2", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO light_rail_lines SELECT * FROM legacy\nspark.sql(\"INSERT INTO community.donations SELECT instid, amountdonated, supplier_company_id, detection_date FROM policy_feedback WHERE instid > 368\")\n", "labels": {"reads": [{"table": "policy_feedback", "columns": ["instid", "amountdonated", "supplier_company_id", "detection_date"]}], "writes": [{"table": "community.donations", "columns": ["instid", "amountdonated", "supplier_company_id", "detection_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table employment --target-dir /tmp/land\n", "labels": {"reads": [{"table": "employment", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO digital_trends SELECT height_feet, course_name, grant_start_date, membergender FROM dws.dws_campaigns_df WHERE height_feet > 270\"], check=True)\n", "labels": {"reads": [{"table": "dws.dws_campaigns_df", "columns": ["height_feet", "course_name", "grant_start_date", "membergender"]}], "writes": [{"table": "digital_trends", "columns": ["height_feet", "course_name", "grant_start_date", "membergender"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.clicks (customer_type_code, funding_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.clicks", "columns": ["customer_type_code", "funding_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.job > 461).all()\n# src table: dws.dws_coupon_use_di\nengine.execute(\"INSERT INTO game_results SELECT * FROM dws.dws_coupon_use_di\")\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_di", "columns": null}], "writes": [{"table": "game_results", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO recyclingprogram SELECT actual_order_id, publication_year FROM pets WHERE actual_order_id > 17\"\n", "labels": {"reads": [{"table": "pets", "columns": ["actual_order_id", "publication_year"]}], "writes": [{"table": "recyclingprogram", "columns": ["actual_order_id", "publication_year"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO marine_life_data SELECT * FROM legacy\ncur.execute(\"SELECT port_code, color_description FROM wins LIMIT 425\")\n", "labels": {"reads": [{"table": "wins", "columns": ["port_code", "color_description"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"yearly_production\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "yearly_production", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"communitydevelopment\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "communitydevelopment", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO habitat SELECT * FROM legacy\ncur.execute(\"SELECT sport, job_title_code FROM farm_competition LIMIT 22\")\n", "labels": {"reads": [{"table": "farm_competition", "columns": ["sport", "job_title_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO voyages SELECT school_name, copy_number, opening_year FROM editor WHERE school_name > 489\"], check=True)\n", "labels": {"reads": [{"table": "editor", "columns": ["school_name", "copy_number", "opening_year"]}], "writes": [{"table": "voyages", "columns": ["school_name", "copy_number", "opening_year"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT poll_source, completed_course FROM shipments LIMIT 7\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "shipments", "columns": ["poll_source", "completed_course"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO dw.dw_sessions_delta SELECT a.coach_id, b.cause_name FROM music_festival a JOIN waterusage b ON a.depth = b.depth\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "music_festival", "columns": null}, {"table": "waterusage", "columns": null}], "writes": [{"table": "dw.dw_sessions_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"defense_contracts_v2\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "defense_contracts_v2", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO us_platforms (improvement, hourid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "us_platforms", "columns": ["improvement", "hourid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"team_members\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "team_members", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"courts\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "courts", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO bi.bi_sessions_hourly SELECT education_id, max_age FROM community_engagement WHERE education_id > 433\");\n", "labels": {"reads": [{"table": "community_engagement", "columns": ["education_id", "max_age"]}], "writes": [{"table": "bi.bi_sessions_hourly", "columns": ["education_id", "max_age"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.member_point\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ads.member_point", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.dissolved_oxygen > 392).all()\n# src table: state_usage\nengine.execute(\"INSERT INTO graduates SELECT * FROM state_usage\")\n", "labels": {"reads": [{"table": "state_usage", "columns": null}], "writes": [{"table": "graduates", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO soilanalysis SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO affordablehousing SELECT trial_name, certification, grade, method_name FROM redundant_billing_data WHERE trial_name > 107\"], check=True)\n", "labels": {"reads": [{"table": "redundant_billing_data", "columns": ["trial_name", "certification", "grade", "method_name"]}], "writes": [{"table": "affordablehousing", "columns": ["trial_name", "certification", "grade", "method_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO discount_coupons SELECT genre_is, movie FROM bi_refunds_daily WHERE genre_is > 269\"\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["genre_is", "movie"]}], "writes": [{"table": "discount_coupons", "columns": ["genre_is", "movie"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO militarydrones SELECT airport_id, case_burden FROM ticket_sales WHERE airport_id > 488\")\n", "labels": {"reads": [{"table": "ticket_sales", "columns": ["airport_id", "case_burden"]}], "writes": [{"table": "militarydrones", "columns": ["airport_id", "case_burden"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table energy_efficiency_programs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "energy_efficiency_programs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO cultural_competency_training SELECT * FROM legacy\ncur.execute(\"SELECT crop_name, accreditation_level FROM workout_sessions LIMIT 469\")\n", "labels": {"reads": [{"table": "workout_sessions", "columns": ["crop_name", "accreditation_level"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO al_jazeera_data SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dw.dw_users_di SELECT draft_class, resolutiondate, energy_star_rating, detection_date FROM mart.mart_coupon_use_df WHERE draft_class > 258\"\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_df", "columns": ["draft_class", "resolutiondate", "energy_star_rating", "detection_date"]}], "writes": [{"table": "dw.dw_users_di", "columns": ["draft_class", "resolutiondate", "energy_star_rating", "detection_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO jp_schema.policy_areas SELECT donator_name, working_horses FROM dwd.dwd_device_log_delta WHERE donator_name > 183\");\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": ["donator_name", "working_horses"]}], "writes": [{"table": "jp_schema.policy_areas", "columns": ["donator_name", "working_horses"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.party_name > 379).all()\n# src table: cultural_competency\nengine.execute(\"INSERT INTO bus_fares SELECT * FROM cultural_competency\")\n", "labels": {"reads": [{"table": "cultural_competency", "columns": null}], "writes": [{"table": "bus_fares", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model inventory_df depends on city_properties\ndbt build -s inventory_df --vars '{\"src\":\"city_properties\"}'\n", "labels": {"reads": [{"table": "city_properties", "columns": null}], "writes": [{"table": "inventory_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.virtual_tour_engagement_time > 229).all()\n# src table: electric_vehicle_stats\nengine.execute(\"INSERT INTO communitypolicingcenters SELECT * FROM electric_vehicle_stats\")\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": null}], "writes": [{"table": "communitypolicingcenters", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO certifications (appointment_duration, blockfloor) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "certifications", "columns": ["appointment_duration", "blockfloor"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table investors --target-dir /tmp/land\n", "labels": {"reads": [{"table": "investors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ucl_top10 depends on yearly_production\ndbt build --models ucl_top10 --vars 'source: yearly_production'\n", "labels": {"reads": [{"table": "yearly_production", "columns": null}], "writes": [{"table": "ucl_top10", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"restaurant_revenue\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"trainers\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "trainers", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stg.orders_daily SELECT mailing_date, departmentname FROM smartcontracts WHERE mailing_date > 171\"\n", "labels": {"reads": [{"table": "smartcontracts", "columns": ["mailing_date", "departmentname"]}], "writes": [{"table": "stg.orders_daily", "columns": ["mailing_date", "departmentname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"artworks\")\nsrc.write.insertInto(\"workers\", overwrite=True)\n", "labels": {"reads": [{"table": "artworks", "columns": null}], "writes": [{"table": "workers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"leo_missions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"customer_master_index\")\n", "labels": {"reads": [{"table": "leo_missions", "columns": null}], "writes": [{"table": "customer_master_index", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods_cart_item_df SELECT stuid, fueldate, thefttype FROM stores WHERE stuid > 370\"\n", "labels": {"reads": [{"table": "stores", "columns": ["stuid", "fueldate", "thefttype"]}], "writes": [{"table": "ods_cart_item_df", "columns": ["stuid", "fueldate", "thefttype"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"eventparticipation\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "eventparticipation", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO carbonoffsetinitiatives (dock_status, overall_rating) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "carbonoffsetinitiatives", "columns": ["dock_status", "overall_rating"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO fairtradecertification SELECT contractor_name, city_population FROM first_notification_of_loss WHERE contractor_name > 444\")\n", "labels": {"reads": [{"table": "first_notification_of_loss", "columns": ["contractor_name", "city_population"]}], "writes": [{"table": "fairtradecertification", "columns": ["contractor_name", "city_population"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO movies (route_short_name, special_features) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "movies", "columns": ["route_short_name", "special_features"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_production\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "food_production", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO representative SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 420;\nSQL\n", "labels": {"reads": [{"table": "ods.member_point_df", "columns": ["quantity_containers", "gold"]}, {"table": "divisions", "columns": ["min_temperature_f", "operationdate", "donorname", "granteeid"]}], "writes": [{"table": "hydro_power", "columns": ["min_temperature_f", "operationdate", "donorname", "granteeid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO furniture SELECT incorporated_in, ratingdate, class_code, title FROM aid_missions WHERE incorporated_in > 422\"], check=True)\n", "labels": {"reads": [{"table": "aid_missions", "columns": ["incorporated_in", "ratingdate", "class_code", "title"]}], "writes": [{"table": "furniture", "columns": ["incorporated_in", "ratingdate", "class_code", "title"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO adrprograms SELECT fan_id, water_consumption FROM lead_mines WHERE fan_id > 91\")\n", "labels": {"reads": [{"table": "lead_mines", "columns": ["fan_id", "water_consumption"]}], "writes": [{"table": "adrprograms", "columns": ["fan_id", "water_consumption"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_payments --columns soil_moisture,guest_last_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_payments", "columns": ["soil_moisture", "guest_last_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 214;\nEOF\n", "labels": {"reads": [{"table": "trainmaintenance", "columns": ["campusfee", "mental_health_status", "total_amount"]}], "writes": [{"table": "circulation_history", "columns": ["campusfee", "mental_health_status", "total_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO agro_regions SELECT attorneyid, community_center_id FROM dwd.dwd_vendors WHERE attorneyid > 202\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": ["attorneyid", "community_center_id"]}], "writes": [{"table": "agro_regions", "columns": ["attorneyid", "community_center_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"productsafety\")\nsave_to_sink(df, \"vocals\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "productsafety", "columns": null}], "writes": [{"table": "vocals", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO rooms SELECT budget_million, time_stamp FROM fruitimport WHERE budget_million > 224\");\n", "labels": {"reads": [{"table": "fruitimport", "columns": ["budget_million", "time_stamp"]}], "writes": [{"table": "rooms", "columns": ["budget_million", "time_stamp"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ods.ods_users_daily\").where(\"dt = current_date()\").writeTo(\"customers_cards\").append()\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": null}], "writes": [{"table": "customers_cards", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"bi.bi_vendors_di\")\nupsert_to_store(df, \"medical_professionals\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.bi_vendors_di", "columns": null}], "writes": [{"table": "medical_professionals", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO travel_advisory (organisation_type, consider_rate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "travel_advisory", "columns": ["organisation_type", "consider_rate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"fraud_detections\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "fraud_detections", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 45;\nSQL\n", "labels": {"reads": [{"table": "matches", "columns": ["funder", "opening_year"]}, {"table": "wind_farms", "columns": ["pages_per_minute_color", "customer_status_code", "trip_start_time", "ai_model"]}], "writes": [{"table": "third_party_companies", "columns": ["pages_per_minute_color", "customer_status_code", "trip_start_time", "ai_model"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT artworkyear, labordate FROM landfills LIMIT 450\")\nrows = cur.fetchall()\nimport logging\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "landfills", "columns": ["artworkyear", "labordate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 227;\nSQL\n", "labels": {"reads": [{"table": "community_development.transactions", "columns": ["ethical_certifications", "gametype"]}, {"table": "maintenance_schedule", "columns": ["route_short_name", "fan_id", "candidate_id", "policy_name"]}], "writes": [{"table": "totalenergyproduction", "columns": ["route_short_name", "fan_id", "candidate_id", "policy_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_table(ctx, \"landfill_capacity_north_america\")\nsave_to_store(df, \"soccer_teams\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "landfill_capacity_north_america", "columns": null}], "writes": [{"table": "soccer_teams", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"refugees\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "refugees", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO initiatives_3 SELECT a.sellingprice, b.max_dew_point_f FROM student_lifelong_learning a JOIN parks b ON a.area_size = b.area_size\"\n", "labels": {"reads": [{"table": "student_lifelong_learning", "columns": null}, {"table": "parks", "columns": null}], "writes": [{"table": "initiatives_3", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model sustainable_practices_2 depends on artifact_analysis\ndbt run --models sustainable_practices_2 --vars '{\"src\":\"artifact_analysis\"}'\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": null}], "writes": [{"table": "sustainable_practices_2", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM restaurant\", conn)\ndf.to_sql(\"claims\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "restaurant", "columns": null}], "writes": [{"table": "claims", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.volunteer_id > 281).all()\n# src table: criminal_justice_reform_initiatives\nengine.execute(\"INSERT INTO restaurant_type SELECT * FROM criminal_justice_reform_initiatives\")\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": null}], "writes": [{"table": "restaurant_type", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.online_dispute_resolution > 467).all()\n# src table: dwd.dwd_payments_full\nengine.execute(\"INSERT INTO staff_department_assignments SELECT * FROM dwd.dwd_payments_full\")\n", "labels": {"reads": [{"table": "dwd.dwd_payments_full", "columns": null}], "writes": [{"table": "staff_department_assignments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO consumer_preference SELECT * FROM legacy\nspark.sql(\"INSERT INTO healthcare_budget SELECT representative_id, spacecraft_model FROM assessment_notes WHERE representative_id > 330\")\n", "labels": {"reads": [{"table": "assessment_notes", "columns": ["representative_id", "spacecraft_model"]}], "writes": [{"table": "healthcare_budget", "columns": ["representative_id", "spacecraft_model"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"farm\").where(\"dt = current_date()\").writeTo(\"assets_frameworks\").append()\n", "labels": {"reads": [{"table": "farm", "columns": null}], "writes": [{"table": "assets_frameworks", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"farmers_india\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "farmers_india", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table tickets --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tickets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO forest_species SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vendorfabrics\").toPandas()\ndf[[\"participation_date\", \"max_depth\"]].to_sql(\"pilot\", engine, index=False)\n", "labels": {"reads": [{"table": "vendorfabrics", "columns": null}], "writes": [{"table": "pilot", "columns": ["participation_date", "max_depth"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"publicchargingstations\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "publicchargingstations", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_transportation\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"user_ad_interactions\")\n", "labels": {"reads": [{"table": "urban_transportation", "columns": null}], "writes": [{"table": "user_ad_interactions", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO development_hours SELECT lastname, snatch, base_name, feature_details FROM virtual_tour_offers WHERE lastname > 487\"\n", "labels": {"reads": [{"table": "virtual_tour_offers", "columns": ["lastname", "snatch", "base_name", "feature_details"]}], "writes": [{"table": "development_hours", "columns": ["lastname", "snatch", "base_name", "feature_details"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO submarine_canyons (min_age, missionid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "submarine_canyons", "columns": ["min_age", "missionid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO furniture SELECT * FROM legacy\nspark.sql(\"INSERT INTO assets_frameworks SELECT experience, amount_due, city_area FROM production WHERE experience > 115\")\n", "labels": {"reads": [{"table": "production", "columns": ["experience", "amount_due", "city_area"]}], "writes": [{"table": "assets_frameworks", "columns": ["experience", "amount_due", "city_area"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO foodaid SELECT healthcareid, date_assigned_to FROM bookings WHERE healthcareid > 33\")\n", "labels": {"reads": [{"table": "bookings", "columns": ["healthcareid", "date_assigned_to"]}], "writes": [{"table": "foodaid", "columns": ["healthcareid", "date_assigned_to"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.payment_method > 96).all()\n# src table: investment_accounts\nengine.execute(\"INSERT INTO african_tourism SELECT * FROM investment_accounts\")\n", "labels": {"reads": [{"table": "investment_accounts", "columns": null}], "writes": [{"table": "african_tourism", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bi.bi_sessions_daily\").where(\"dt = current_date()\").writeTo(\"canada_cosmetics_preferences\").append()\n", "labels": {"reads": [{"table": "bi.bi_sessions_daily", "columns": null}], "writes": [{"table": "canada_cosmetics_preferences", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM user_workouts_march\"\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO accommodations (document_structure_code, species_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "accommodations", "columns": ["document_structure_code", "species_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"wta_serves\").where(\"dt = current_date()\").writeTo(\"victims\").append()\n", "labels": {"reads": [{"table": "wta_serves", "columns": null}], "writes": [{"table": "victims", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table document_functional_areas --columns game,incidentid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "document_functional_areas", "columns": ["game", "incidentid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO mart.campaigns_full SELECT * FROM legacy\nspark.sql(\"INSERT INTO platformh SELECT bicycle_id, donationid FROM project_timeline WHERE bicycle_id > 2\")\n", "labels": {"reads": [{"table": "project_timeline", "columns": ["bicycle_id", "donationid"]}], "writes": [{"table": "platformh", "columns": ["bicycle_id", "donationid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO fault_log_parts SELECT funding_received, survey_id FROM nba WHERE funding_received > 490\")\n", "labels": {"reads": [{"table": "nba", "columns": ["funding_received", "survey_id"]}], "writes": [{"table": "fault_log_parts", "columns": ["funding_received", "survey_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO review (dockingid, temperature) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "review", "columns": ["dockingid", "temperature"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO storage_tech SELECT initiative_region, donationamount FROM iron_ore_production WHERE initiative_region > 490\")\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": ["initiative_region", "donationamount"]}], "writes": [{"table": "storage_tech", "columns": ["initiative_region", "donationamount"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO stg.stg_inventory_full SELECT movieid, rating, trainingtype, ll_id FROM tournaments WHERE movieid > 28\")\n", "labels": {"reads": [{"table": "tournaments", "columns": ["movieid", "rating", "trainingtype", "ll_id"]}], "writes": [{"table": "stg.stg_inventory_full", "columns": ["movieid", "rating", "trainingtype", "ll_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ods.ods_campaigns_df\", conn)\ndf.to_sql(\"habitat3\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_df", "columns": null}], "writes": [{"table": "habitat3", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO available_policies SELECT course_type, wifi, donation_amount, jul FROM dwd_risk_score_hourly WHERE course_type > 285\"\n", "labels": {"reads": [{"table": "dwd_risk_score_hourly", "columns": ["course_type", "wifi", "donation_amount", "jul"]}], "writes": [{"table": "available_policies", "columns": ["course_type", "wifi", "donation_amount", "jul"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO artist_info SELECT * FROM legacy\ncur.execute(\"SELECT contract_end_date, participant_details FROM artcontributors LIMIT 177\")\n", "labels": {"reads": [{"table": "artcontributors", "columns": ["contract_end_date", "participant_details"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"otas\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "otas", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"fish_purchases\")\nsink_to_warehouse(df, \"patient_outcomes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fish_purchases", "columns": null}], "writes": [{"table": "patient_outcomes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model malicious_activity depends on domesticconferences\ndbt run --select malicious_activity --vars '{\"src\":\"domesticconferences\"}'\n", "labels": {"reads": [{"table": "domesticconferences", "columns": null}], "writes": [{"table": "malicious_activity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT savings, contributorid FROM has_allergy LIMIT 193\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO onlineengagement SELECT environmental_impact_score, game_genre, target_id FROM game_results WHERE environmental_impact_score > 235\")\n", "labels": {"reads": [{"table": "has_allergy", "columns": ["savings", "contributorid"]}, {"table": "game_results", "columns": ["environmental_impact_score", "game_genre", "target_id"]}], "writes": [{"table": "onlineengagement", "columns": ["environmental_impact_score", "game_genre", "target_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO policyadvocacyevents SELECT a.investment_round, b.premises_type FROM web_client_accelerator a JOIN trade_history b ON a.requestdate = b.requestdate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "web_client_accelerator", "columns": null}, {"table": "trade_history", "columns": null}], "writes": [{"table": "policyadvocacyevents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO crypto_transactions SELECT fundingamount, participation_id FROM mart.mart_payments_delta WHERE fundingamount > 332\")\n", "labels": {"reads": [{"table": "mart.mart_payments_delta", "columns": ["fundingamount", "participation_id"]}], "writes": [{"table": "crypto_transactions", "columns": ["fundingamount", "participation_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.vendors_delta SELECT resource_type, recorded_by_staff_id, completed, menu_name FROM participants WHERE resource_type > 178\"], check=True)\n", "labels": {"reads": [{"table": "participants", "columns": ["resource_type", "recorded_by_staff_id", "completed", "menu_name"]}], "writes": [{"table": "ads.vendors_delta", "columns": ["resource_type", "recorded_by_staff_id", "completed", "menu_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM intelligence_agency\"\n", "labels": {"reads": [{"table": "intelligence_agency", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model reviews depends on artistsales\ndbt run -s reviews --vars '{\"source_table\":\"artistsales\"}'\n", "labels": {"reads": [{"table": "artistsales", "columns": null}], "writes": [{"table": "reviews", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO school_roster (mine_type, aid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "school_roster", "columns": ["mine_type", "aid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO artifact_analysis SELECT incident_description, athlete_id, preferred_foot FROM engineer_skills WHERE incident_description > 204\")\n", "labels": {"reads": [{"table": "engineer_skills", "columns": ["incident_description", "athlete_id", "preferred_foot"]}], "writes": [{"table": "artifact_analysis", "columns": ["incident_description", "athlete_id", "preferred_foot"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO co2_emission SELECT instructor_id, room_number, exhibitionid, winning_aircraft FROM school_bus WHERE instructor_id > 199\")\n", "labels": {"reads": [{"table": "school_bus", "columns": ["instructor_id", "room_number", "exhibitionid", "winning_aircraft"]}], "writes": [{"table": "co2_emission", "columns": ["instructor_id", "room_number", "exhibitionid", "winning_aircraft"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO wearable_metrics SELECT task_id, transit_passengers, building_address FROM displaced_people WHERE task_id > 378\"], check=True)\n", "labels": {"reads": [{"table": "displaced_people", "columns": ["task_id", "transit_passengers", "building_address"]}], "writes": [{"table": "wearable_metrics", "columns": ["task_id", "transit_passengers", "building_address"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO donationhistory SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bank\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads.ads_cart_item_hourly\")\n", "labels": {"reads": [{"table": "bank", "columns": null}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO opioid_overdoses SELECT room_count, shoe_brand, products_this_year, emp_id FROM crop_temperature WHERE room_count > 227\"\n", "labels": {"reads": [{"table": "crop_temperature", "columns": ["room_count", "shoe_brand", "products_this_year", "emp_id"]}], "writes": [{"table": "opioid_overdoses", "columns": ["room_count", "shoe_brand", "products_this_year", "emp_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM sourcing\"\n", "labels": {"reads": [{"table": "sourcing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO shoes SELECT production_id, advisoryid, inclusivehousing, player_id FROM sustainable_building WHERE production_id > 39\"\n", "labels": {"reads": [{"table": "sustainable_building", "columns": ["production_id", "advisoryid", "inclusivehousing", "player_id"]}], "writes": [{"table": "shoes", "columns": ["production_id", "advisoryid", "inclusivehousing", "player_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.ads_shipments_delta SELECT coach_name, recordid FROM justice_schemas.legal_tech_providers WHERE coach_name > 184\"\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": ["coach_name", "recordid"]}], "writes": [{"table": "ads.ads_shipments_delta", "columns": ["coach_name", "recordid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 479;\nSQL\n", "labels": {"reads": [{"table": "public.trips_by_day_train", "columns": ["word_count", "fastestlapspeed"]}, {"table": "equipment_sales", "columns": ["builder", "day_of_week", "played"]}], "writes": [{"table": "recycledmaterialsgarments", "columns": ["builder", "day_of_week", "played"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table bi.bi_risk_score_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.bi_risk_score_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO climate_adaptation_projects SELECT actor_name, country_code, propertyid FROM bi_refunds_daily WHERE actor_name > 182\"\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["actor_name", "country_code", "propertyid"]}], "writes": [{"table": "climate_adaptation_projects", "columns": ["actor_name", "country_code", "propertyid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO public.crime_types SELECT workshop_name, maxoccupancy, investment_round, cultivatorname FROM nba_games WHERE workshop_name > 308\")\n", "labels": {"reads": [{"table": "nba_games", "columns": ["workshop_name", "maxoccupancy", "investment_round", "cultivatorname"]}], "writes": [{"table": "public.crime_types", "columns": ["workshop_name", "maxoccupancy", "investment_round", "cultivatorname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM on_call\", conn)\ndf.to_sql(\"flu_shots\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "on_call", "columns": null}], "writes": [{"table": "flu_shots", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.users_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO infantmortalitydata SELECT tech_type, dispensary_id, payment_method_code, minister FROM regulatory_frameworks WHERE tech_type > 345\"], check=True)\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": ["tech_type", "dispensary_id", "payment_method_code", "minister"]}], "writes": [{"table": "infantmortalitydata", "columns": ["tech_type", "dispensary_id", "payment_method_code", "minister"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO student SELECT years_working, num_hotels FROM smart_grids WHERE years_working > 409\"\n", "labels": {"reads": [{"table": "smart_grids", "columns": ["years_working", "num_hotels"]}], "writes": [{"table": "student", "columns": ["years_working", "num_hotels"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO militaryequipment SELECT checkout, membership_type, visitid, dlocation FROM transportation_union WHERE checkout > 410\"\n", "labels": {"reads": [{"table": "transportation_union", "columns": ["checkout", "membership_type", "visitid", "dlocation"]}], "writes": [{"table": "militaryequipment", "columns": ["checkout", "membership_type", "visitid", "dlocation"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"accessibility_audits\");\ndf.write().mode(\"overwrite\").saveAsTable(\"supportservices\");\n", "labels": {"reads": [{"table": "accessibility_audits", "columns": null}], "writes": [{"table": "supportservices", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM defense_projects_sales\"\n", "labels": {"reads": [{"table": "defense_projects_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT organization_name, workouttype FROM vehiclemodels LIMIT 49\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": ["organization_name", "workouttype"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT complaint_date, incident_date FROM user_profiles LIMIT 438\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "user_profiles", "columns": ["complaint_date", "incident_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO prison SELECT centerid, cloud_cover FROM topublictransportation WHERE centerid > 218\")\n", "labels": {"reads": [{"table": "topublictransportation", "columns": ["centerid", "cloud_cover"]}], "writes": [{"table": "prison", "columns": ["centerid", "cloud_cover"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO takes SELECT launch_date, energy_production FROM dams WHERE launch_date > 337\")\n", "labels": {"reads": [{"table": "dams", "columns": ["launch_date", "energy_production"]}], "writes": [{"table": "takes", "columns": ["launch_date", "energy_production"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cmi_details, continent_id FROM satellite_deployment LIMIT 137\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "satellite_deployment", "columns": ["cmi_details", "continent_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO company SELECT investmentdate, score FROM communitydevelopment WHERE investmentdate > 279\"\n", "labels": {"reads": [{"table": "communitydevelopment", "columns": ["investmentdate", "score"]}], "writes": [{"table": "company", "columns": ["investmentdate", "score"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"electric_taxis\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"recreation_centers\")\n", "labels": {"reads": [{"table": "electric_taxis", "columns": null}], "writes": [{"table": "recreation_centers", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"carbon_pricing\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "carbon_pricing", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dw.shipments_di --columns average_age,facid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dw.shipments_di", "columns": ["average_age", "facid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO mart.mart_vendors SELECT is_accessible, starting_year, guest_first_name, therapy_type FROM user_ad_interactions WHERE is_accessible > 2\"\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": ["is_accessible", "starting_year", "guest_first_name", "therapy_type"]}], "writes": [{"table": "mart.mart_vendors", "columns": ["is_accessible", "starting_year", "guest_first_name", "therapy_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.registration_id > 270).all()\n# src table: feed\nengine.execute(\"INSERT INTO traffic SELECT * FROM feed\")\n", "labels": {"reads": [{"table": "feed", "columns": null}], "writes": [{"table": "traffic", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO casesbyyear SELECT a.crop, b.primaryaffiliation FROM stg.stg_events_di a JOIN bi.bi_payments b ON a.contractorid = b.contractorid\"\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": null}, {"table": "bi.bi_payments", "columns": null}], "writes": [{"table": "casesbyyear", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO atlantic_ocean SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO restaurant (account_number, launch_agency) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "restaurant", "columns": ["account_number", "launch_agency"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mart.shipments_delta\").where(\"dt = current_date()\").writeTo(\"ads.ads_risk_score_hourly\").append()\n", "labels": {"reads": [{"table": "mart.shipments_delta", "columns": null}], "writes": [{"table": "ads.ads_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model defense_contractors depends on ytterbium_supply\ndbt run -s defense_contractors --vars '{\"source_table\":\"ytterbium_supply\"}'\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": null}], "writes": [{"table": "defense_contractors", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.recruitername > 170).all()\n# src table: ods_vendors_daily\nengine.execute(\"INSERT INTO ods.sessions SELECT * FROM ods_vendors_daily\")\n", "labels": {"reads": [{"table": "ods_vendors_daily", "columns": null}], "writes": [{"table": "ods.sessions", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO resource_extraction SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 333;\nEOF\n", "labels": {"reads": [{"table": "communitypolicing", "columns": ["assigned_to_staff_id", "implementation_date", "workout_date", "median_home_value"]}], "writes": [{"table": "playergamedata", "columns": ["assigned_to_staff_id", "implementation_date", "workout_date", "median_home_value"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"music_streaming\").toPandas()\ndf[[\"mean_sea_level_pressure_inches\", \"incident_region\"]].to_sql(\"youth_fan_participation\", engine, index=False)\n", "labels": {"reads": [{"table": "music_streaming", "columns": null}], "writes": [{"table": "youth_fan_participation", "columns": ["mean_sea_level_pressure_inches", "incident_region"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO product_sales SELECT accreditation_type, org, individual_first_name FROM agriculturalinvestments WHERE accreditation_type > 496\"\n", "labels": {"reads": [{"table": "agriculturalinvestments", "columns": ["accreditation_type", "org", "individual_first_name"]}], "writes": [{"table": "product_sales", "columns": ["accreditation_type", "org", "individual_first_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO contracts SELECT a.cuisine_id, b.therapy_date FROM bike_stations a JOIN rental b ON a.end_station_id = b.end_station_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bike_stations", "columns": null}, {"table": "rental", "columns": null}], "writes": [{"table": "contracts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"customer_contact_channels\")\nsrc.write.insertInto(\"weekly_weather\", overwrite=True)\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": null}], "writes": [{"table": "weekly_weather", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"doctors\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "doctors", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dw.inventory_delta SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT therapy_sessions, production_usage FROM bi.bi_orders_delta LIMIT 165\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "bi.bi_orders_delta", "columns": ["therapy_sessions", "production_usage"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO spacemissions SELECT materialtype, investment_name FROM shop WHERE materialtype > 483\"\n", "labels": {"reads": [{"table": "shop", "columns": ["materialtype", "investment_name"]}], "writes": [{"table": "spacemissions", "columns": ["materialtype", "investment_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO lenders SELECT a.awayteamid, b.fan_id FROM branch a JOIN miningwaterusage b ON a.project_name = b.project_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "branch", "columns": null}, {"table": "miningwaterusage", "columns": null}], "writes": [{"table": "lenders", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"areas\").toPandas()\ndf[[\"interest_group\", \"actor_id\"]].to_sql(\"tryout\", engine, index=False)\n", "labels": {"reads": [{"table": "areas", "columns": null}], "writes": [{"table": "tryout", "columns": ["interest_group", "actor_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table route --target-dir /tmp/land\n", "labels": {"reads": [{"table": "route", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO store_district SELECT * FROM legacy\ncur.execute(\"SELECT rank, product_type FROM chemicals LIMIT 393\")\n", "labels": {"reads": [{"table": "chemicals", "columns": ["rank", "product_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO innovation_projects (users_engaged, plant_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "innovation_projects", "columns": ["users_engaged", "plant_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO vessel_capacity SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM marketingbudget\", conn)\ndf.to_sql(\"sustainable_projects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "marketingbudget", "columns": null}], "writes": [{"table": "sustainable_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"wearable_metrics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"agricultural_innovation_projects\")\n", "labels": {"reads": [{"table": "wearable_metrics", "columns": null}], "writes": [{"table": "agricultural_innovation_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table fabricdata --target-dir /tmp/land\n", "labels": {"reads": [{"table": "fabricdata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT device_id, apid FROM ods_shipments_df LIMIT 165\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "ods_shipments_df", "columns": ["device_id", "apid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table part_faults --target-dir /tmp/land\n", "labels": {"reads": [{"table": "part_faults", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads (profession, tech) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads", "columns": ["profession", "tech"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO defense_contracts SELECT stream_id, departure_date FROM esportsevents WHERE stream_id > 418\")\n", "labels": {"reads": [{"table": "esportsevents", "columns": ["stream_id", "departure_date"]}], "writes": [{"table": "defense_contracts", "columns": ["stream_id", "departure_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"broadband_customers_global\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 92;\nEOF\n", "labels": {"reads": [{"table": "bioprocesses", "columns": ["address_details", "grant_start_date"]}], "writes": [{"table": "smartcities", "columns": ["address_details", "grant_start_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sales_by_quarter SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO electric_taxis SELECT * FROM legacy\ncur.execute(\"SELECT origin_city, year_founded FROM soil_moisture LIMIT 153\")\n", "labels": {"reads": [{"table": "soil_moisture", "columns": ["origin_city", "year_founded"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 242;\nSQL\n", "labels": {"reads": [{"table": "tickets_3", "columns": ["railway_id", "jan"]}, {"table": "transport", "columns": ["distance", "customer_status_code", "guest_id"]}], "writes": [{"table": "marine_species", "columns": ["distance", "customer_status_code", "guest_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"tourist_attraction_features\")\nwrite_to_target(df, \"france_culture\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tourist_attraction_features", "columns": null}], "writes": [{"table": "france_culture", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO mart_cart_item_di SELECT review_score, producerid, dno, art_type FROM workforce_training WHERE review_score > 320\")\n", "labels": {"reads": [{"table": "workforce_training", "columns": ["review_score", "producerid", "dno", "art_type"]}], "writes": [{"table": "mart_cart_item_di", "columns": ["review_score", "producerid", "dno", "art_type"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"strains\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "strains", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO vesselfuel SELECT factory, change_date, event_date FROM organic_farms WHERE factory > 307\")\n", "labels": {"reads": [{"table": "organic_farms", "columns": ["factory", "change_date", "event_date"]}], "writes": [{"table": "vesselfuel", "columns": ["factory", "change_date", "event_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods_clicks_df SELECT * FROM legacy\ncur.execute(\"SELECT feedid, grant_date FROM cloud_issues LIMIT 50\")\n", "labels": {"reads": [{"table": "cloud_issues", "columns": ["feedid", "grant_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table thefttypes --columns offset_id,address_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "thefttypes", "columns": ["offset_id", "address_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO scientists SELECT is_valid, credit_score, capacity_percentage, container_count FROM cultural_heritage WHERE is_valid > 251\"\n", "labels": {"reads": [{"table": "cultural_heritage", "columns": ["is_valid", "credit_score", "capacity_percentage", "container_count"]}], "writes": [{"table": "scientists", "columns": ["is_valid", "credit_score", "capacity_percentage", "container_count"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 98;\nEOF\n", "labels": {"reads": [{"table": "continent", "columns": ["rest_id", "attendee_age", "gamesplayed"]}], "writes": [{"table": "epl_teams", "columns": ["rest_id", "attendee_age", "gamesplayed"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO smart_cities SELECT avg_speed, church_id FROM complaints WHERE avg_speed > 213\")\n", "labels": {"reads": [{"table": "complaints", "columns": ["avg_speed", "church_id"]}], "writes": [{"table": "smart_cities", "columns": ["avg_speed", "church_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO wind_farms SELECT application_date, serviceid FROM pollution_initiatives WHERE application_date > 131\")\n", "labels": {"reads": [{"table": "pollution_initiatives", "columns": ["application_date", "serviceid"]}], "writes": [{"table": "wind_farms", "columns": ["application_date", "serviceid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO regional_archaeologists SELECT * FROM legacy\ncur.execute(\"SELECT num_libraries, home_games FROM hotel_reviews LIMIT 139\")\n", "labels": {"reads": [{"table": "hotel_reviews", "columns": ["num_libraries", "home_games"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table artwork --columns founder_group,home_team --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "artwork", "columns": ["founder_group", "home_team"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO causes SELECT * FROM legacy\nspark.sql(\"INSERT INTO traditional_arts SELECT investment, prof_num, pallet_id, call_count FROM ads.ads_risk_score_hourly WHERE investment > 132\")\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": ["investment", "prof_num", "pallet_id", "call_count"]}], "writes": [{"table": "traditional_arts", "columns": ["investment", "prof_num", "pallet_id", "call_count"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO dwd.dwd_payments_di SELECT purchaseid, station_id, virtual_tour_sessions, measurement FROM streams WHERE purchaseid > 91\")\n", "labels": {"reads": [{"table": "streams", "columns": ["purchaseid", "station_id", "virtual_tour_sessions", "measurement"]}], "writes": [{"table": "dwd.dwd_payments_di", "columns": ["purchaseid", "station_id", "virtual_tour_sessions", "measurement"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"coral_reefs\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"billstatus\")\n", "labels": {"reads": [{"table": "coral_reefs", "columns": null}], "writes": [{"table": "billstatus", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 325;\nEOF\n", "labels": {"reads": [{"table": "dws.exposure_df", "columns": ["case_number", "price_in_dollar"]}], "writes": [{"table": "chemical_concentration", "columns": ["case_number", "price_in_dollar"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM student_access\", conn)\ndf.to_sql(\"agroecology_practices\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "student_access", "columns": null}], "writes": [{"table": "agroecology_practices", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.bi_exposure_hourly SELECT timestamp, users_engaged, sales_amount, nurse FROM mountain WHERE timestamp > 206\"], check=True)\n", "labels": {"reads": [{"table": "mountain", "columns": ["timestamp", "users_engaged", "sales_amount", "nurse"]}], "writes": [{"table": "bi.bi_exposure_hourly", "columns": ["timestamp", "users_engaged", "sales_amount", "nurse"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"directors\")\nsrc.write.insertInto(\"shops\", overwrite=True)\n", "labels": {"reads": [{"table": "directors", "columns": null}], "writes": [{"table": "shops", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO chemicals_annual (grant_amount, fuelconsumed) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "chemicals_annual", "columns": ["grant_amount", "fuelconsumed"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table audience_demographics --columns dates_active,skill_description --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "audience_demographics", "columns": ["dates_active", "skill_description"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO virtual_tour_revenue SELECT job_category, wheels, article_id FROM volunteer_events WHERE job_category > 63\");\n", "labels": {"reads": [{"table": "volunteer_events", "columns": ["job_category", "wheels", "article_id"]}], "writes": [{"table": "virtual_tour_revenue", "columns": ["job_category", "wheels", "article_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO fossil_fuel_vehicles_japan SELECT * FROM legacy\nspark.sql(\"INSERT INTO sustainable_urban_properties_2 SELECT schedule_date, width FROM technician WHERE schedule_date > 430\")\n", "labels": {"reads": [{"table": "technician", "columns": ["schedule_date", "width"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["schedule_date", "width"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO police_stations SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM investors\"\n", "labels": {"reads": [{"table": "investors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO aircraftsquadrons SELECT practicename, ticket_id, subscriber_id, fare_amount FROM feed WHERE practicename > 451\"], check=True)\n", "labels": {"reads": [{"table": "feed", "columns": ["practicename", "ticket_id", "subscriber_id", "fare_amount"]}], "writes": [{"table": "aircraftsquadrons", "columns": ["practicename", "ticket_id", "subscriber_id", "fare_amount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 322;\nSQL\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": ["sustainability_rating", "town_city"]}, {"table": "docking", "columns": ["vendor_id", "teamname", "trip_type"]}], "writes": [{"table": "lead_mines", "columns": ["vendor_id", "teamname", "trip_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart_orders_di SELECT a.document_name, b.order_item_id FROM rating a JOIN camera_lens b ON a.lastclaimdate = b.lastclaimdate\"\n", "labels": {"reads": [{"table": "rating", "columns": null}, {"table": "camera_lens", "columns": null}], "writes": [{"table": "mart_orders_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO trip SELECT a.hours_played, b.billing_amount FROM cloud_issues a JOIN carbonoffsetinitiatives b ON a.program_id = b.program_id\"\n", "labels": {"reads": [{"table": "cloud_issues", "columns": null}, {"table": "carbonoffsetinitiatives", "columns": null}], "writes": [{"table": "trip", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO patients SELECT founded_year, building_short_name, vol_id, silver FROM student_courses WHERE founded_year > 101\"\n", "labels": {"reads": [{"table": "student_courses", "columns": ["founded_year", "building_short_name", "vol_id", "silver"]}], "writes": [{"table": "patients", "columns": ["founded_year", "building_short_name", "vol_id", "silver"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attraction_type_description, astronaut FROM participants\", engine)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\ndf.to_sql(\"bi.risk_score_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "participants", "columns": ["attraction_type_description", "astronaut"]}], "writes": [{"table": "bi.risk_score_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table drilling_rigs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "drilling_rigs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marketingbudget\").toPandas()\ndf[[\"startup_id\", \"hireid\"]].to_sql(\"infrastructurebudget\", engine, index=False)\n", "labels": {"reads": [{"table": "marketingbudget", "columns": null}], "writes": [{"table": "infrastructurebudget", "columns": ["startup_id", "hireid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM creativeais\", conn)\ndf.to_sql(\"equipment_sales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "creativeais", "columns": null}], "writes": [{"table": "equipment_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 193;\nSQL\n", "labels": {"reads": [{"table": "public_schools", "columns": ["stadium_id", "fund_type"]}, {"table": "labor_hours", "columns": ["violationid", "cmi_details", "playerid", "driver_id"]}], "writes": [{"table": "mammals", "columns": ["violationid", "cmi_details", "playerid", "driver_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.fine_amount > 370).all()\n# src table: habitats\nengine.execute(\"INSERT INTO esportsteamsafrica SELECT * FROM habitats\")\n", "labels": {"reads": [{"table": "habitats", "columns": null}], "writes": [{"table": "esportsteamsafrica", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO spacecrafts SELECT emergency_type, date_valid_from, site_id FROM intelligencesatellites WHERE emergency_type > 438\")\n", "labels": {"reads": [{"table": "intelligencesatellites", "columns": ["emergency_type", "date_valid_from", "site_id"]}], "writes": [{"table": "spacecrafts", "columns": ["emergency_type", "date_valid_from", "site_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO opendatainitiatives SELECT * FROM legacy\ncur.execute(\"SELECT dishname, issued_date FROM disabilitysupportprograms LIMIT 379\")\n", "labels": {"reads": [{"table": "disabilitysupportprograms", "columns": ["dishname", "issued_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibitionsartworks\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"skincareinventory\")\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": null}], "writes": [{"table": "skincareinventory", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO mart.mart_payments_hourly SELECT attribute_id, spf_level, entryid, location_id FROM dwd.dwd_events_delta WHERE attribute_id > 7\");\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": ["attribute_id", "spf_level", "entryid", "location_id"]}], "writes": [{"table": "mart.mart_payments_hourly", "columns": ["attribute_id", "spf_level", "entryid", "location_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO mart.risk_score_df SELECT airline, party_email, department_id, org_size FROM bi.risk_score_df WHERE airline > 408\")\n", "labels": {"reads": [{"table": "bi.risk_score_df", "columns": ["airline", "party_email", "department_id", "org_size"]}], "writes": [{"table": "mart.risk_score_df", "columns": ["airline", "party_email", "department_id", "org_size"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 427;\nEOF\n", "labels": {"reads": [{"table": "supportprograms", "columns": ["heritage_site", "player", "trench_name"]}], "writes": [{"table": "greenbuildings", "columns": ["heritage_site", "player", "trench_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO dw.dw_coupon_use_daily SELECT strategy_id, license_id, supplier, athlete_id FROM heritage_sites WHERE strategy_id > 120\");\n", "labels": {"reads": [{"table": "heritage_sites", "columns": ["strategy_id", "license_id", "supplier", "athlete_id"]}], "writes": [{"table": "dw.dw_coupon_use_daily", "columns": ["strategy_id", "license_id", "supplier", "athlete_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.member_point_full\").toPandas()\ndf[[\"neighborhoodid\", \"building_name\"]].to_sql(\"rural_development.agriculture_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": null}], "writes": [{"table": "rural_development.agriculture_projects", "columns": ["neighborhoodid", "building_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM rural_infrastructure\"\n", "labels": {"reads": [{"table": "rural_infrastructure", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.stg_exposure_daily SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO genderdistribution (district_id, workers) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "genderdistribution", "columns": ["district_id", "workers"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"field\")\nsrc.write.insertInto(\"mart.vendors_full\", overwrite=True)\n", "labels": {"reads": [{"table": "field", "columns": null}], "writes": [{"table": "mart.vendors_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"rebounds\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"militarydrones\")\n", "labels": {"reads": [{"table": "rebounds", "columns": null}], "writes": [{"table": "militarydrones", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO products_in_events SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"financialwellbeing\")\nsrc.write.insertInto(\"sustainable_warehouses\", overwrite=True)\n", "labels": {"reads": [{"table": "financialwellbeing", "columns": null}], "writes": [{"table": "sustainable_warehouses", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO donationhistory SELECT year_deforested, casetype FROM traffic_citations WHERE year_deforested > 117\"\n", "labels": {"reads": [{"table": "traffic_citations", "columns": ["year_deforested", "casetype"]}], "writes": [{"table": "donationhistory", "columns": ["year_deforested", "casetype"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 51;\nEOF\n", "labels": {"reads": [{"table": "dws_coupon_use_df", "columns": ["booking_end_date", "development_type"]}], "writes": [{"table": "rural_resources", "columns": ["booking_end_date", "development_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vrusers SELECT mine_name, painting_name FROM climate_adaptation WHERE mine_name > 468\"\n", "labels": {"reads": [{"table": "climate_adaptation", "columns": ["mine_name", "painting_name"]}], "writes": [{"table": "vrusers", "columns": ["mine_name", "painting_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO trucks SELECT led_by, call_id, review_id FROM sustainable_urban_properties_2 WHERE led_by > 307\")\n", "labels": {"reads": [{"table": "sustainable_urban_properties_2", "columns": ["led_by", "call_id", "review_id"]}], "writes": [{"table": "trucks", "columns": ["led_by", "call_id", "review_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bridgeconstruction SELECT lieutenant_governor, policy, safetytestdate FROM traffic_violations WHERE lieutenant_governor > 474\"\n", "labels": {"reads": [{"table": "traffic_violations", "columns": ["lieutenant_governor", "policy", "safetytestdate"]}], "writes": [{"table": "bridgeconstruction", "columns": ["lieutenant_governor", "policy", "safetytestdate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO user_stats SELECT year_built, issue_month, union_member_id, retailer FROM new_schedules WHERE year_built > 490\"\n", "labels": {"reads": [{"table": "new_schedules", "columns": ["year_built", "issue_month", "union_member_id", "retailer"]}], "writes": [{"table": "user_stats", "columns": ["year_built", "issue_month", "union_member_id", "retailer"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table skills --target-dir /tmp/land\n", "labels": {"reads": [{"table": "skills", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO rural_feeder_roads SELECT * FROM legacy\nspark.sql(\"INSERT INTO supportprograms SELECT num_of_component, permit_date FROM sales_quarterly WHERE num_of_component > 457\")\n", "labels": {"reads": [{"table": "sales_quarterly", "columns": ["num_of_component", "permit_date"]}], "writes": [{"table": "supportprograms", "columns": ["num_of_component", "permit_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table immunizationrates --columns instructor,production_qty --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "immunizationrates", "columns": ["instructor", "production_qty"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ticketsales SELECT publication_id, document_structure_description, publication_year FROM agriculturalinnovations WHERE publication_id > 11\"\n", "labels": {"reads": [{"table": "agriculturalinnovations", "columns": ["publication_id", "document_structure_description", "publication_year"]}], "writes": [{"table": "ticketsales", "columns": ["publication_id", "document_structure_description", "publication_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table bi.bi_shipments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.bi_shipments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"comments\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "comments", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT complaintid, gtype FROM public.police_calls\", engine)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"dws.dws_inventory_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "public.police_calls", "columns": ["complaintid", "gtype"]}], "writes": [{"table": "dws.dws_inventory_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws_coupon_use_df\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dws_coupon_use_df", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO purchases (mediatypeid, rehab_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "purchases", "columns": ["mediatypeid", "rehab_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 277;\nSQL\n", "labels": {"reads": [{"table": "fertilizer", "columns": ["trackid", "nurse"]}, {"table": "athletes", "columns": ["followers", "session_date", "employeename"]}], "writes": [{"table": "conditions", "columns": ["followers", "session_date", "employeename"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model professional_development depends on teacher_development_race\ndbt build --select professional_development --vars '{\"source_table\":\"teacher_development_race\"}'\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": null}], "writes": [{"table": "professional_development", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"communityengagements\").toPandas()\ndf[[\"restypedescription\", \"prod_id\"]].to_sql(\"pitstops\", engine, index=False)\n", "labels": {"reads": [{"table": "communityengagements", "columns": null}], "writes": [{"table": "pitstops", "columns": ["restypedescription", "prod_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO ads.ads_shipments_delta SELECT player_id, engagement_count, surname FROM useracct WHERE player_id > 50\");\n", "labels": {"reads": [{"table": "useracct", "columns": ["player_id", "engagement_count", "surname"]}], "writes": [{"table": "ads.ads_shipments_delta", "columns": ["player_id", "engagement_count", "surname"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO measurements SELECT hospitalname, deliveryid FROM organizations WHERE hospitalname > 217\"\n", "labels": {"reads": [{"table": "organizations", "columns": ["hospitalname", "deliveryid"]}], "writes": [{"table": "measurements", "columns": ["hospitalname", "deliveryid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"shipment\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "shipment", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"temperatureanomalies\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"tech_accessibility_funding\")\n", "labels": {"reads": [{"table": "temperatureanomalies", "columns": null}], "writes": [{"table": "tech_accessibility_funding", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"equipment_maintenance\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ods.ods_exposure_delta\")\n", "labels": {"reads": [{"table": "equipment_maintenance", "columns": null}], "writes": [{"table": "ods.ods_exposure_delta", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT mining_operation_id, news_outlet FROM coal_reserves LIMIT 138\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO dws.exposure SELECT rental_rate, circularsupplychain, budget_type_description, store_name FROM coralreefs WHERE rental_rate > 417\")\n", "labels": {"reads": [{"table": "coal_reserves", "columns": ["mining_operation_id", "news_outlet"]}, {"table": "coralreefs", "columns": ["rental_rate", "circularsupplychain", "budget_type_description", "store_name"]}], "writes": [{"table": "dws.exposure", "columns": ["rental_rate", "circularsupplychain", "budget_type_description", "store_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM student\"\n", "labels": {"reads": [{"table": "student", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM customerorders\"\n", "labels": {"reads": [{"table": "customerorders", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"renewable_power\");\ndf.write().mode(\"overwrite\").saveAsTable(\"species_forests\");\n", "labels": {"reads": [{"table": "renewable_power", "columns": null}], "writes": [{"table": "species_forests", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO department_publications SELECT countryid, round_amount FROM device_usage WHERE countryid > 315\"\n", "labels": {"reads": [{"table": "device_usage", "columns": ["countryid", "round_amount"]}], "writes": [{"table": "department_publications", "columns": ["countryid", "round_amount"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO artprograms SELECT catalog_id, end_speed, gender_group, user_id FROM spending WHERE catalog_id > 355\");\n", "labels": {"reads": [{"table": "spending", "columns": ["catalog_id", "end_speed", "gender_group", "user_id"]}], "writes": [{"table": "artprograms", "columns": ["catalog_id", "end_speed", "gender_group", "user_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"music_streaming\").where(\"dt = current_date()\").writeTo(\"contract_transactions\").append()\n", "labels": {"reads": [{"table": "music_streaming", "columns": null}], "writes": [{"table": "contract_transactions", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi_products\"\n", "labels": {"reads": [{"table": "bi_products", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO thefttypes SELECT host_city, num_libraries, state_id, orgid FROM tour_guides WHERE host_city > 76\"\n", "labels": {"reads": [{"table": "tour_guides", "columns": ["host_city", "num_libraries", "state_id", "orgid"]}], "writes": [{"table": "thefttypes", "columns": ["host_city", "num_libraries", "state_id", "orgid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"museums\")\nexport_to_sink(df, \"students_enrollment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "museums", "columns": null}], "writes": [{"table": "students_enrollment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO indie_artists SELECT watch_time, fish_id, orderid FROM libraries WHERE watch_time > 233\")\n", "labels": {"reads": [{"table": "libraries", "columns": ["watch_time", "fish_id", "orderid"]}], "writes": [{"table": "indie_artists", "columns": ["watch_time", "fish_id", "orderid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT organisation_details, payment_method FROM sustainability\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"attendee_demographics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "sustainability", "columns": ["organisation_details", "payment_method"]}], "writes": [{"table": "attendee_demographics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO prepaid_mobile SELECT time_of_day, matchdate, itemname FROM sportsinfo WHERE time_of_day > 413\"\n", "labels": {"reads": [{"table": "sportsinfo", "columns": ["time_of_day", "matchdate", "itemname"]}], "writes": [{"table": "prepaid_mobile", "columns": ["time_of_day", "matchdate", "itemname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"economic_diversification_efforts\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table electric_buses --target-dir /tmp/land\n", "labels": {"reads": [{"table": "electric_buses", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO paris_train SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO ads.ads_shipments_delta SELECT aid_name, asset_name, founded_year, offender_name FROM heritagesites WHERE aid_name > 177\");\n", "labels": {"reads": [{"table": "heritagesites", "columns": ["aid_name", "asset_name", "founded_year", "offender_name"]}], "writes": [{"table": "ads.ads_shipments_delta", "columns": ["aid_name", "asset_name", "founded_year", "offender_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"sustainability_fact\")\nsrc.write.insertInto(\"ods.products_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "sustainability_fact", "columns": null}], "writes": [{"table": "ods.products_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"phishing_attempts\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "phishing_attempts", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_finance_organizations\"\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 334;\nEOF\n", "labels": {"reads": [{"table": "digitalliteracytraining", "columns": ["sessionid", "inclusivehousing"]}], "writes": [{"table": "design_standards", "columns": ["sessionid", "inclusivehousing"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"indie_artists\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"rnd_budget\")\n", "labels": {"reads": [{"table": "indie_artists", "columns": null}], "writes": [{"table": "rnd_budget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO sustainablebrands SELECT contributions, preferred_foot FROM mart.inventory_hourly WHERE contributions > 443\")\n", "labels": {"reads": [{"table": "mart.inventory_hourly", "columns": ["contributions", "preferred_foot"]}], "writes": [{"table": "sustainablebrands", "columns": ["contributions", "preferred_foot"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO veterans SELECT farmer_id, status_code, pixels, media_type_id FROM mammals WHERE farmer_id > 20\"\n", "labels": {"reads": [{"table": "mammals", "columns": ["farmer_id", "status_code", "pixels", "media_type_id"]}], "writes": [{"table": "veterans", "columns": ["farmer_id", "status_code", "pixels", "media_type_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ll_hours, volunteerjoindate FROM building_permits LIMIT 249\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "building_permits", "columns": ["ll_hours", "volunteerjoindate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO manufacturermaterials SELECT college_location, researcher_name FROM submersible_dives WHERE college_location > 145\"\n", "labels": {"reads": [{"table": "submersible_dives", "columns": ["college_location", "researcher_name"]}], "writes": [{"table": "manufacturermaterials", "columns": ["college_location", "researcher_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO funding_records SELECT circularsupplychain, organisation_id, energy_consumption FROM project_timeline WHERE circularsupplychain > 286\"\n", "labels": {"reads": [{"table": "project_timeline", "columns": ["circularsupplychain", "organisation_id", "energy_consumption"]}], "writes": [{"table": "funding_records", "columns": ["circularsupplychain", "organisation_id", "energy_consumption"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ods.ods_member_point_delta (driver_id, spf_level) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ods.ods_member_point_delta", "columns": ["driver_id", "spf_level"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO bi.bi_inventory_delta SELECT velocity, contractid, factory_id, genre FROM acidification_data WHERE velocity > 102\"\n", "labels": {"reads": [{"table": "acidification_data", "columns": ["velocity", "contractid", "factory_id", "genre"]}], "writes": [{"table": "bi.bi_inventory_delta", "columns": ["velocity", "contractid", "factory_id", "genre"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO art_exhibit_attendance (user_account, performance_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "art_exhibit_attendance", "columns": ["user_account", "performance_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table solar_farms --target-dir /tmp/land\n", "labels": {"reads": [{"table": "solar_farms", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT acidification_level, catalog_entry_name FROM safetyincidents LIMIT 316\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "safetyincidents", "columns": ["acidification_level", "catalog_entry_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_orders_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"property_community\")\n", "labels": {"reads": [{"table": "bi_orders_daily", "columns": null}], "writes": [{"table": "property_community", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO climatedata SELECT a.appointment_duration, b.startyear FROM ingredient a JOIN store_product b ON a.funding_source = b.funding_source\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ingredient", "columns": null}, {"table": "store_product", "columns": null}], "writes": [{"table": "climatedata", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO recycled_polyester SELECT * FROM legacy\ncur.execute(\"SELECT fare_id, forest_id FROM recyclers LIMIT 136\")\n", "labels": {"reads": [{"table": "recyclers", "columns": ["fare_id", "forest_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT drug_name, vaccination_status FROM indigenous_communities LIMIT 350\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO ads_cart_item_hourly SELECT license_number, num_investments, quantity_sold, address_type_code FROM dws.dws_coupon_use_full WHERE license_number > 494\")\n", "labels": {"reads": [{"table": "indigenous_communities", "columns": ["drug_name", "vaccination_status"]}, {"table": "dws.dws_coupon_use_full", "columns": ["license_number", "num_investments", "quantity_sold", "address_type_code"]}], "writes": [{"table": "ads_cart_item_hourly", "columns": ["license_number", "num_investments", "quantity_sold", "address_type_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM apac_hotel_views\"\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model investmentsesg depends on shrimp_farms\ndbt build -s investmentsesg --vars '{\"src\":\"shrimp_farms\"}'\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": null}], "writes": [{"table": "investmentsesg", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model employeedata depends on algorithmic_fairness_incidents\ndbt build --models employeedata --vars 'source: algorithmic_fairness_incidents'\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents", "columns": null}], "writes": [{"table": "employeedata", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO graduates SELECT art, ota_id FROM cybersecurity_incidents WHERE art > 31\"\n", "labels": {"reads": [{"table": "cybersecurity_incidents", "columns": ["art", "ota_id"]}], "writes": [{"table": "graduates", "columns": ["art", "ota_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"platform\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "platform", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"soilmoisturedata\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"customer_transactions\")\n", "labels": {"reads": [{"table": "soilmoisturedata", "columns": null}], "writes": [{"table": "customer_transactions", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cmi_details, closuredate FROM bi.refunds_daily LIMIT 172\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "bi.refunds_daily", "columns": ["cmi_details", "closuredate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table language --target-dir /tmp/land\n", "labels": {"reads": [{"table": "language", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO school_districts SELECT * FROM legacy\ncur.execute(\"SELECT cultivatorname, centerid FROM mentalhealthproviders LIMIT 196\")\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": ["cultivatorname", "centerid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 377;\nEOF\n", "labels": {"reads": [{"table": "authenticationlogs", "columns": ["strain_type", "allergytype", "requestdate"]}], "writes": [{"table": "skincare_sales", "columns": ["strain_type", "allergytype", "requestdate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO instructor SELECT elimination_move, innovation_id FROM stock WHERE elimination_move > 147\"\n", "labels": {"reads": [{"table": "stock", "columns": ["elimination_move", "innovation_id"]}], "writes": [{"table": "instructor", "columns": ["elimination_move", "innovation_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model recalls depends on conservation_programs\ndbt run --models recalls --vars '{\"src\":\"conservation_programs\"}'\n", "labels": {"reads": [{"table": "conservation_programs", "columns": null}], "writes": [{"table": "recalls", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT updatedate, quantity_sold FROM performers\", engine)\nmetrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"document_functional_areas\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "performers", "columns": ["updatedate", "quantity_sold"]}], "writes": [{"table": "document_functional_areas", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthparityscores\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"skincare_sales\")\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": null}], "writes": [{"table": "skincare_sales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table associatedheritages --target-dir /tmp/land\n", "labels": {"reads": [{"table": "associatedheritages", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"management\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"hospitallocations\")\n", "labels": {"reads": [{"table": "management", "columns": null}], "writes": [{"table": "hospitallocations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewable_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"smartcitytech\")\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}], "writes": [{"table": "smartcitytech", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.bi_inventory_delta SELECT traveler_id, submission_id, art_movement, strain_type FROM exhibitionattendance WHERE traveler_id > 6\"], check=True)\n", "labels": {"reads": [{"table": "exhibitionattendance", "columns": ["traveler_id", "submission_id", "art_movement", "strain_type"]}], "writes": [{"table": "bi.bi_inventory_delta", "columns": ["traveler_id", "submission_id", "art_movement", "strain_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO fireincidents SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_table(ctx, \"platformi\")\nsave_to_output(df, \"premises\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "platformi", "columns": null}], "writes": [{"table": "premises", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO digitalliteracytraining SELECT local_authority, party_id, center_name, fundingid FROM student_course_registrations WHERE local_authority > 43\")\n", "labels": {"reads": [{"table": "student_course_registrations", "columns": ["local_authority", "party_id", "center_name", "fundingid"]}], "writes": [{"table": "digitalliteracytraining", "columns": ["local_authority", "party_id", "center_name", "fundingid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO climateresearch SELECT percentage_change, sustainability_score, rating_id, stay FROM cargo_data WHERE percentage_change > 433\");\n", "labels": {"reads": [{"table": "cargo_data", "columns": ["percentage_change", "sustainability_score", "rating_id", "stay"]}], "writes": [{"table": "climateresearch", "columns": ["percentage_change", "sustainability_score", "rating_id", "stay"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO asteroids SELECT price, campus, retweets FROM vrusers WHERE price > 151\");\n", "labels": {"reads": [{"table": "vrusers", "columns": ["price", "campus", "retweets"]}], "writes": [{"table": "asteroids", "columns": ["price", "campus", "retweets"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 272;\nSQL\n", "labels": {"reads": [{"table": "ai_papers", "columns": ["bus_id", "report"]}, {"table": "worker_scores", "columns": ["phase", "animal_name", "extraction_state"]}], "writes": [{"table": "fabricinventory", "columns": ["phase", "animal_name", "extraction_state"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"lenders\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ai_safety\");\n", "labels": {"reads": [{"table": "lenders", "columns": null}], "writes": [{"table": "ai_safety", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO carbonoffsetinitiatives SELECT omim, account_type FROM exam_results WHERE omim > 50\")\n", "labels": {"reads": [{"table": "exam_results", "columns": ["omim", "account_type"]}], "writes": [{"table": "carbonoffsetinitiatives", "columns": ["omim", "account_type"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO prereq SELECT transit_passengers, num_songs FROM parity_violations WHERE transit_passengers > 379\"], check=True)\n", "labels": {"reads": [{"table": "parity_violations", "columns": ["transit_passengers", "num_songs"]}], "writes": [{"table": "prereq", "columns": ["transit_passengers", "num_songs"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"teacher_pd\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws.dws_cart_item_daily\");\n", "labels": {"reads": [{"table": "teacher_pd", "columns": null}], "writes": [{"table": "dws.dws_cart_item_daily", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 355;\nSQL\n", "labels": {"reads": [{"table": "member_attendance", "columns": ["segment_id", "supplier_name"]}, {"table": "bi_shipments_daily", "columns": ["billingamount", "dept_store_id", "mappinglength"]}], "writes": [{"table": "organization", "columns": ["billingamount", "dept_store_id", "mappinglength"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthparityviolations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mentalhealthparityviolations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO exhibitions SELECT white, revenueid, workoutid FROM crypto_transactions WHERE white > 436\");\n", "labels": {"reads": [{"table": "crypto_transactions", "columns": ["white", "revenueid", "workoutid"]}], "writes": [{"table": "exhibitions", "columns": ["white", "revenueid", "workoutid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO smart_city_projects SELECT daily_visitors, address, characteristic_type_code, call_time FROM aquaculture_farms WHERE daily_visitors > 86\"\n", "labels": {"reads": [{"table": "aquaculture_farms", "columns": ["daily_visitors", "address", "characteristic_type_code", "call_time"]}], "writes": [{"table": "smart_city_projects", "columns": ["daily_visitors", "address", "characteristic_type_code", "call_time"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table artwork --target-dir /tmp/land\n", "labels": {"reads": [{"table": "artwork", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ancient_artifacts (mine_name, product_stock_number) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ancient_artifacts", "columns": ["mine_name", "product_stock_number"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"clothingsales\")\nsrc.write.insertInto(\"waterconservationinitiatives\", overwrite=True)\n", "labels": {"reads": [{"table": "clothingsales", "columns": null}], "writes": [{"table": "waterconservationinitiatives", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.years_operating > 360).all()\n# src table: tree_habitat_associations\nengine.execute(\"INSERT INTO red_line SELECT * FROM tree_habitat_associations\")\n", "labels": {"reads": [{"table": "tree_habitat_associations", "columns": null}], "writes": [{"table": "red_line", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dwd.dwd_campaigns_df\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT volunteer_name, rows FROM engineer_visits LIMIT 132\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO auto_shows SELECT rooms, restypedescription, bedtype, is_organic FROM mars_spacecraft WHERE rooms > 272\")\n", "labels": {"reads": [{"table": "engineer_visits", "columns": ["volunteer_name", "rows"]}, {"table": "mars_spacecraft", "columns": ["rooms", "restypedescription", "bedtype", "is_organic"]}], "writes": [{"table": "auto_shows", "columns": ["rooms", "restypedescription", "bedtype", "is_organic"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO electric_vehicles SELECT a.courses, b.investor_details FROM fund_investments a JOIN mental_health_professionals_2 b ON a.business_size = b.business_size\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fund_investments", "columns": null}, {"table": "mental_health_professionals_2", "columns": null}], "writes": [{"table": "electric_vehicles", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM workforce_training\"\n", "labels": {"reads": [{"table": "workforce_training", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"has_allergy\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"farm\")\n", "labels": {"reads": [{"table": "has_allergy", "columns": null}], "writes": [{"table": "farm", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"tb_reports\")\nupsert_to_store(df, \"dwd.dwd_member_point_full\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tb_reports", "columns": null}], "writes": [{"table": "dwd.dwd_member_point_full", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO galleries SELECT area_id, longitude FROM cargo_handling WHERE area_id > 67\"\n", "labels": {"reads": [{"table": "cargo_handling", "columns": ["area_id", "longitude"]}], "writes": [{"table": "galleries", "columns": ["area_id", "longitude"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_table(ctx, \"languages\")\npush_to_warehouse(df, \"stg.stg_risk_score\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "languages", "columns": null}], "writes": [{"table": "stg.stg_risk_score", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM hotel_revenue\"\n", "labels": {"reads": [{"table": "hotel_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO climate_finance_re (party_email, platform) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "climate_finance_re", "columns": ["party_email", "platform"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"smart_city_projects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "smart_city_projects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT made_in_usa, closuredate FROM genetics.projects LIMIT 64\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [{"table": "genetics.projects", "columns": ["made_in_usa", "closuredate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"tree_habitat_associations\")\nsrc.write.insertInto(\"passenger_trips\", overwrite=True)\n", "labels": {"reads": [{"table": "tree_habitat_associations", "columns": null}], "writes": [{"table": "passenger_trips", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO mediatype SELECT a.songname, b.brand_id FROM royal_family a JOIN attack_outcomes b ON a.activity_date = b.activity_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "royal_family", "columns": null}, {"table": "attack_outcomes", "columns": null}], "writes": [{"table": "mediatype", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO public_participation SELECT a.other_characteristic_details, b.activity FROM sustainable_urban a JOIN insurancetype b ON a.exit_type = b.exit_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sustainable_urban", "columns": null}, {"table": "insurancetype", "columns": null}], "writes": [{"table": "public_participation", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO noise_pollution SELECT produceid, customer_first_name FROM ods.ods_device_log_delta WHERE produceid > 107\");\n", "labels": {"reads": [{"table": "ods.ods_device_log_delta", "columns": ["produceid", "customer_first_name"]}], "writes": [{"table": "noise_pollution", "columns": ["produceid", "customer_first_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO disabilityadvocacy SELECT launch_agency, maintenance_id, has_disability, production_date FROM public.forest_stats WHERE launch_agency > 400\")\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": ["launch_agency", "maintenance_id", "has_disability", "production_date"]}], "writes": [{"table": "disabilityadvocacy", "columns": ["launch_agency", "maintenance_id", "has_disability", "production_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_life_populations\").toPandas()\ndf[[\"spent\", \"menu_item\"]].to_sql(\"artcollection\", engine, index=False)\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": null}], "writes": [{"table": "artcollection", "columns": ["spent", "menu_item"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM food_assistance\"\n", "labels": {"reads": [{"table": "food_assistance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO russia_nato_diplomacy (indigenous, population) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "russia_nato_diplomacy", "columns": ["indigenous", "population"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ocean_depths\").where(\"dt = current_date()\").writeTo(\"trees\").append()\n", "labels": {"reads": [{"table": "ocean_depths", "columns": null}], "writes": [{"table": "trees", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO country_sustainable_chains (hub_id, nurse) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "country_sustainable_chains", "columns": ["hub_id", "nurse"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO donor (co2_reduction, college) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "donor", "columns": ["co2_reduction", "college"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cell_mobile_number, coowner_name FROM concert_revenue LIMIT 71\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "concert_revenue", "columns": ["cell_mobile_number", "coowner_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"vesselarrivals\")\ndump_to_output(df, \"mart_cart_item_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": null}], "writes": [{"table": "mart_cart_item_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO cotton_source SELECT transit_passengers, program_type, carrierid, jobtitle FROM assignedto WHERE transit_passengers > 199\"\n", "labels": {"reads": [{"table": "assignedto", "columns": ["transit_passengers", "program_type", "carrierid", "jobtitle"]}], "writes": [{"table": "cotton_source", "columns": ["transit_passengers", "program_type", "carrierid", "jobtitle"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO tourism_centers SELECT sales_amount, call_time, chemicalid FROM login_attempts WHERE sales_amount > 170\"\n", "labels": {"reads": [{"table": "login_attempts", "columns": ["sales_amount", "call_time", "chemicalid"]}], "writes": [{"table": "tourism_centers", "columns": ["sales_amount", "call_time", "chemicalid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table investment_strategies --columns drug,attendance --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "investment_strategies", "columns": ["drug", "attendance"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO market SELECT inspectionscore, underrepresented_community, employment_id FROM labor_unions WHERE inspectionscore > 73\"], check=True)\n", "labels": {"reads": [{"table": "labor_unions", "columns": ["inspectionscore", "underrepresented_community", "employment_id"]}], "writes": [{"table": "market", "columns": ["inspectionscore", "underrepresented_community", "employment_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO drought_data SELECT male_id, user_rating FROM wellbeing_programs WHERE male_id > 487\"\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": ["male_id", "user_rating"]}], "writes": [{"table": "drought_data", "columns": ["male_id", "user_rating"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dw_risk_score_full depends on stg.stg_campaigns\ndbt build -s dw_risk_score_full --vars '{\"src\":\"stg.stg_campaigns\"}'\n", "labels": {"reads": [{"table": "stg.stg_campaigns", "columns": null}], "writes": [{"table": "dw_risk_score_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO union_membership SELECT * FROM legacy\nspark.sql(\"INSERT INTO military_bases SELECT no_of_loans, booking_end_date, detention_summary, founding_year FROM eventparticipation WHERE no_of_loans > 257\")\n", "labels": {"reads": [{"table": "eventparticipation", "columns": ["no_of_loans", "booking_end_date", "detention_summary", "founding_year"]}], "writes": [{"table": "military_bases", "columns": ["no_of_loans", "booking_end_date", "detention_summary", "founding_year"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO co2price (src_apid, state) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "co2price", "columns": ["src_apid", "state"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"skincareinventory\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"benefits_overpayments\")\n", "labels": {"reads": [{"table": "skincareinventory", "columns": null}], "writes": [{"table": "benefits_overpayments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT production_id, ai_algorithm_id FROM construction_labor LIMIT 201\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "construction_labor", "columns": ["production_id", "ai_algorithm_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO crime_incidents SELECT dribbling, gamename FROM restorative_justice_center WHERE dribbling > 305\")\n", "labels": {"reads": [{"table": "restorative_justice_center", "columns": ["dribbling", "gamename"]}], "writes": [{"table": "crime_incidents", "columns": ["dribbling", "gamename"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"veteran_occupations\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "veteran_occupations", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dws.dws_refunds_hourly\");\ndf.write().mode(\"overwrite\").saveAsTable(\"product_characteristics\");\n", "labels": {"reads": [{"table": "dws.dws_refunds_hourly", "columns": null}], "writes": [{"table": "product_characteristics", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"park\")\nupsert_to_warehouse(df, \"gamesessions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "park", "columns": null}], "writes": [{"table": "gamesessions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"animal_budget\");\ndf.write().mode(\"overwrite\").saveAsTable(\"game_results\");\n", "labels": {"reads": [{"table": "animal_budget", "columns": null}], "writes": [{"table": "game_results", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO spaceradar (train_id, completion_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "spaceradar", "columns": ["train_id", "completion_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"uniteddefense.equipmentsales\");\ndf.write().mode(\"overwrite\").saveAsTable(\"overwatch_scores\");\n", "labels": {"reads": [{"table": "uniteddefense.equipmentsales", "columns": null}], "writes": [{"table": "overwatch_scores", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO tryout SELECT race_ethnicity, bus_number FROM plots WHERE race_ethnicity > 275\"\n", "labels": {"reads": [{"table": "plots", "columns": ["race_ethnicity", "bus_number"]}], "writes": [{"table": "tryout", "columns": ["race_ethnicity", "bus_number"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"open_data_initiatives\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "open_data_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO province.human_rights_data SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model units depends on unionnegotiations\ndbt build --models units --vars 'source: unionnegotiations'\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": null}], "writes": [{"table": "units", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw_risk_score_daily\")\nsrc.write.insertInto(\"forest_species\", overwrite=True)\n", "labels": {"reads": [{"table": "dw_risk_score_daily", "columns": null}], "writes": [{"table": "forest_species", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model family_cases depends on equipment_maintenance\ndbt run -s family_cases --vars '{\"source_table\":\"equipment_maintenance\"}'\n", "labels": {"reads": [{"table": "equipment_maintenance", "columns": null}], "writes": [{"table": "family_cases", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.detected_at > 282).all()\n# src table: public.trips_by_day_train\nengine.execute(\"INSERT INTO dispensary_sales SELECT * FROM public.trips_by_day_train\")\n", "labels": {"reads": [{"table": "public.trips_by_day_train", "columns": null}], "writes": [{"table": "dispensary_sales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO financial_transactions SELECT * FROM legacy\nspark.sql(\"INSERT INTO esportsteamsafrica SELECT activity, factory FROM renewable_power WHERE activity > 155\")\n", "labels": {"reads": [{"table": "renewable_power", "columns": ["activity", "factory"]}], "writes": [{"table": "esportsteamsafrica", "columns": ["activity", "factory"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO dwd.coupon_use_full SELECT case_id, potency, party FROM convictions WHERE case_id > 324\");\n", "labels": {"reads": [{"table": "convictions", "columns": ["case_id", "potency", "party"]}], "writes": [{"table": "dwd.coupon_use_full", "columns": ["case_id", "potency", "party"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dws.dws_users_hourly SELECT sensor_type, headquarter, debate_id, amount_piad FROM price_data WHERE sensor_type > 46\"\n", "labels": {"reads": [{"table": "price_data", "columns": ["sensor_type", "headquarter", "debate_id", "amount_piad"]}], "writes": [{"table": "dws.dws_users_hourly", "columns": ["sensor_type", "headquarter", "debate_id", "amount_piad"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nimport logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO australia_offset_programs SELECT updated_at, chromosome, billingcountry FROM humanitarian_aid WHERE updated_at > 454\"\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": ["updated_at", "chromosome", "billingcountry"]}], "writes": [{"table": "australia_offset_programs", "columns": ["updated_at", "chromosome", "billingcountry"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"music_streaming\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"features\")\n", "labels": {"reads": [{"table": "music_streaming", "columns": null}], "writes": [{"table": "features", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.stg_events_hourly SELECT line_1_number_building, home_city, causeid FROM stg.stg_inventory_hourly WHERE line_1_number_building > 197\"\n", "labels": {"reads": [{"table": "stg.stg_inventory_hourly", "columns": ["line_1_number_building", "home_city", "causeid"]}], "writes": [{"table": "stg.stg_events_hourly", "columns": ["line_1_number_building", "home_city", "causeid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 13;\nSQL\n", "labels": {"reads": [{"table": "menu_item", "columns": ["emission_date", "menu_name"]}, {"table": "parks", "columns": ["business_name", "customername", "event_date"]}], "writes": [{"table": "invoice_lines", "columns": ["business_name", "customername", "event_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO sustainability_fact SELECT dock_count, membername FROM contract_negotiations WHERE dock_count > 112\")\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": ["dock_count", "membername"]}], "writes": [{"table": "sustainability_fact", "columns": ["dock_count", "membername"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO miningwaterusage SELECT start_date, attack_count FROM safety_records WHERE start_date > 404\"\n", "labels": {"reads": [{"table": "safety_records", "columns": ["start_date", "attack_count"]}], "writes": [{"table": "miningwaterusage", "columns": ["start_date", "attack_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO initiatives (deliveryid, sales) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "initiatives", "columns": ["deliveryid", "sales"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"habitat\").where(\"dt = current_date()\").writeTo(\"waste\").append()\n", "labels": {"reads": [{"table": "habitat", "columns": null}], "writes": [{"table": "waste", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO greenbuildings SELECT * FROM legacy\nspark.sql(\"INSERT INTO train_station SELECT farm_id, chemical_name, fouls, policyholderid FROM election WHERE farm_id > 262\")\n", "labels": {"reads": [{"table": "election", "columns": ["farm_id", "chemical_name", "fouls", "policyholderid"]}], "writes": [{"table": "train_station", "columns": ["farm_id", "chemical_name", "fouls", "policyholderid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM constructors\", conn)\ndf.to_sql(\"ads.ads_users_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "constructors", "columns": null}], "writes": [{"table": "ads.ads_users_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO laptimes (restaurant_name, label) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "laptimes", "columns": ["restaurant_name", "label"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 54;\nEOF\n", "labels": {"reads": [{"table": "tree_species", "columns": ["services", "donation_year", "conferenceid"]}], "writes": [{"table": "schools", "columns": ["services", "donation_year", "conferenceid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 432;\nEOF\n", "labels": {"reads": [{"table": "architect", "columns": ["hospitalid", "participated_in_open_pedagogy", "stream_id", "vaccine_type"]}], "writes": [{"table": "product_categories", "columns": ["hospitalid", "participated_in_open_pedagogy", "stream_id", "vaccine_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM customers_cards\", conn)\ndf.to_sql(\"dwd.dwd_payments_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "customers_cards", "columns": null}], "writes": [{"table": "dwd.dwd_payments_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"india_solar_power\");\ndf.write().mode(\"overwrite\").saveAsTable(\"satellite_missions_large\");\n", "labels": {"reads": [{"table": "india_solar_power", "columns": null}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dates (programtype, accelerator_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dates", "columns": ["programtype", "accelerator_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO ads.ads_inventory_df SELECT bill_id, project_type FROM innovation_grants WHERE bill_id > 117\")\n", "labels": {"reads": [{"table": "innovation_grants", "columns": ["bill_id", "project_type"]}], "writes": [{"table": "ads.ads_inventory_df", "columns": ["bill_id", "project_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"volunteer_hours\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table products --target-dir /tmp/land\n", "labels": {"reads": [{"table": "products", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO singer SELECT a.city_traffic_speed, b.menu_name FROM artifact_analysis a JOIN ship b ON a.image_date = b.image_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": null}, {"table": "ship", "columns": null}], "writes": [{"table": "singer", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT product_size, platform FROM ancient_artifacts LIMIT 164\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO customer_contact_channels SELECT worker_name, maintenance_contract_id, black, year_founded FROM brandrevenue WHERE worker_name > 283\")\n", "labels": {"reads": [{"table": "ancient_artifacts", "columns": ["product_size", "platform"]}, {"table": "brandrevenue", "columns": ["worker_name", "maintenance_contract_id", "black", "year_founded"]}], "writes": [{"table": "customer_contact_channels", "columns": ["worker_name", "maintenance_contract_id", "black", "year_founded"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO open_data_initiatives SELECT incident_type_description, home_team_id, destroyed_by_employee_id, assessmentdate FROM job_postings WHERE incident_type_description > 343\");\n", "labels": {"reads": [{"table": "job_postings", "columns": ["incident_type_description", "home_team_id", "destroyed_by_employee_id", "assessmentdate"]}], "writes": [{"table": "open_data_initiatives", "columns": ["incident_type_description", "home_team_id", "destroyed_by_employee_id", "assessmentdate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO drought_data SELECT a.clinic_type, b.theftdate FROM retail_workers_union a JOIN vendors b ON a.programtype = b.programtype\"\n", "labels": {"reads": [{"table": "retail_workers_union", "columns": null}, {"table": "vendors", "columns": null}], "writes": [{"table": "drought_data", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vesselarrivals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_input(ctx, \"community_education_programs\")\ndump_to_target(df, \"exit_strategy\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "community_education_programs", "columns": null}], "writes": [{"table": "exit_strategy", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table communityengagementmetrics --columns athlete_name,num_of_staff --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "communityengagementmetrics", "columns": ["athlete_name", "num_of_staff"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO heritage_sites_3 SELECT famous_title, school_id, entryid FROM satisfaction WHERE famous_title > 405\"\n", "labels": {"reads": [{"table": "satisfaction", "columns": ["famous_title", "school_id", "entryid"]}], "writes": [{"table": "heritage_sites_3", "columns": ["famous_title", "school_id", "entryid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO daily_oil_production (support_id, mental_health_rating) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "daily_oil_production", "columns": ["support_id", "mental_health_rating"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_orders_delta SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO workerbuildings SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO emergency_calls SELECT checkout, form_type_code FROM farm_competition WHERE checkout > 256\");\n", "labels": {"reads": [{"table": "farm_competition", "columns": ["checkout", "form_type_code"]}], "writes": [{"table": "emergency_calls", "columns": ["checkout", "form_type_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"daily_revenue\");\ndf.write().mode(\"overwrite\").saveAsTable(\"genetics.experiments\");\n", "labels": {"reads": [{"table": "daily_revenue", "columns": null}], "writes": [{"table": "genetics.experiments", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"safetytests\")\nsrc.write.insertInto(\"africa_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "safetytests", "columns": null}], "writes": [{"table": "africa_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO song SELECT characteristic_name, fault_short_name, testtypeid FROM mental_health_professionals_2 WHERE characteristic_name > 142\")\n", "labels": {"reads": [{"table": "mental_health_professionals_2", "columns": ["characteristic_name", "fault_short_name", "testtypeid"]}], "writes": [{"table": "song", "columns": ["characteristic_name", "fault_short_name", "testtypeid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO nutritionfacts SELECT supporter, creationyear, date_of_latest_logon, products_this_year FROM dwd.dwd_vendors WHERE supporter > 411\"\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": ["supporter", "creationyear", "date_of_latest_logon", "products_this_year"]}], "writes": [{"table": "nutritionfacts", "columns": ["supporter", "creationyear", "date_of_latest_logon", "products_this_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model device_log_hourly depends on dallas_fire_incidents\ndbt build --models device_log_hourly --vars '{\"source_table\":\"dallas_fire_incidents\"}'\n", "labels": {"reads": [{"table": "dallas_fire_incidents", "columns": null}], "writes": [{"table": "device_log_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO climate_finance SELECT * FROM legacy\nspark.sql(\"INSERT INTO space_telescopes SELECT donation_id, recorded_by_staff_id, donor_name, dnumber FROM sustainableproduction WHERE donation_id > 194\")\n", "labels": {"reads": [{"table": "sustainableproduction", "columns": ["donation_id", "recorded_by_staff_id", "donor_name", "dnumber"]}], "writes": [{"table": "space_telescopes", "columns": ["donation_id", "recorded_by_staff_id", "donor_name", "dnumber"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO machinery SELECT worker, heart_rate, species_id FROM ai_projects WHERE worker > 418\");\n", "labels": {"reads": [{"table": "ai_projects", "columns": ["worker", "heart_rate", "species_id"]}], "writes": [{"table": "machinery", "columns": ["worker", "heart_rate", "species_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT visitorid, date_of_enrolment FROM dwd.products_di LIMIT 320\")\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO travel_advisory SELECT dispensary_id, fish_id, num_volunteers FROM department_store_chain WHERE dispensary_id > 285\")\n", "labels": {"reads": [{"table": "dwd.products_di", "columns": ["visitorid", "date_of_enrolment"]}, {"table": "department_store_chain", "columns": ["dispensary_id", "fish_id", "num_volunteers"]}], "writes": [{"table": "travel_advisory", "columns": ["dispensary_id", "fish_id", "num_volunteers"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"book\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "book", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 396;\nSQL\n", "labels": {"reads": [{"table": "virtual_tours_oceania", "columns": ["languageid", "payment_type_code"]}, {"table": "workout", "columns": ["actual_order_id", "email"]}], "writes": [{"table": "brand_info", "columns": ["actual_order_id", "email"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sustainable_materials SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"farm\")\nsrc.write.insertInto(\"stg.refunds_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "farm", "columns": null}], "writes": [{"table": "stg.refunds_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT risk_level, investment_id FROM agri_innov LIMIT 97\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "agri_innov", "columns": ["risk_level", "investment_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.campaigns_df\"\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"humanitarian_aid\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"adaptation_projects\")\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": null}], "writes": [{"table": "adaptation_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"continents\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "continents", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"platformstats\").toPandas()\ndf[[\"thefttypeid\", \"status\"]].to_sql(\"african_tourism\", engine, index=False)\n", "labels": {"reads": [{"table": "platformstats", "columns": null}], "writes": [{"table": "african_tourism", "columns": ["thefttypeid", "status"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"provider_training\").where(\"dt = current_date()\").writeTo(\"fish_suppliers\").append()\n", "labels": {"reads": [{"table": "provider_training", "columns": null}], "writes": [{"table": "fish_suppliers", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stg.stg_risk_score\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM continent\"\n", "labels": {"reads": [{"table": "continent", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.stg_risk_score SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO climateresearch SELECT a.practices, b.clinic_id FROM permit a JOIN postseason b ON a.decision = b.decision\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "permit", "columns": null}, {"table": "postseason", "columns": null}], "writes": [{"table": "climateresearch", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO units SELECT sales_details, service_type_code, bridgetype FROM carbon_emissions WHERE sales_details > 88\");\n", "labels": {"reads": [{"table": "carbon_emissions", "columns": ["sales_details", "service_type_code", "bridgetype"]}], "writes": [{"table": "units", "columns": ["sales_details", "service_type_code", "bridgetype"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO drug_approvals SELECT developer_id, totalprice, courtname FROM employees WHERE developer_id > 110\")\n", "labels": {"reads": [{"table": "employees", "columns": ["developer_id", "totalprice", "courtname"]}], "writes": [{"table": "drug_approvals", "columns": ["developer_id", "totalprice", "courtname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ship_agent SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 184;\nSQL\n", "labels": {"reads": [{"table": "pipelines", "columns": ["post_category", "budget_million"]}, {"table": "vendorfabrics", "columns": ["case_outcome", "siteid", "cinema_id", "date_in_locaton_to"]}], "writes": [{"table": "musical", "columns": ["case_outcome", "siteid", "cinema_id", "date_in_locaton_to"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO virtual_tours SELECT * FROM legacy\ncur.execute(\"SELECT scoreid, mental_health_rating FROM catalog_contents_additional_attributes LIMIT 197\")\n", "labels": {"reads": [{"table": "catalog_contents_additional_attributes", "columns": ["scoreid", "mental_health_rating"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT products_this_year, trip_end_time FROM heritage_sites\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"ytterbium_supply\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "heritage_sites", "columns": ["products_this_year", "trip_end_time"]}], "writes": [{"table": "ytterbium_supply", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"lawyers\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "lawyers", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO daily_industrial_water_usage SELECT average_attendance, devices FROM mentalhealthprofessional WHERE average_attendance > 198\")\n", "labels": {"reads": [{"table": "mentalhealthprofessional", "columns": ["average_attendance", "devices"]}], "writes": [{"table": "daily_industrial_water_usage", "columns": ["average_attendance", "devices"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO conservation_programs SELECT complaint_id, garment_id, instid, functional_area_description FROM emergency_categories WHERE complaint_id > 185\"\n", "labels": {"reads": [{"table": "emergency_categories", "columns": ["complaint_id", "garment_id", "instid", "functional_area_description"]}], "writes": [{"table": "conservation_programs", "columns": ["complaint_id", "garment_id", "instid", "functional_area_description"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT doctor_id, sessiondate FROM climate_adaptation LIMIT 149\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "climate_adaptation", "columns": ["doctor_id", "sessiondate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM audience_demographics\", conn)\ndf.to_sql(\"benefits_overpayments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "audience_demographics", "columns": null}], "writes": [{"table": "benefits_overpayments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO communitycourts SELECT contractid, operationdate FROM navalequipmentmaintenance WHERE contractid > 446\"\n", "labels": {"reads": [{"table": "navalequipmentmaintenance", "columns": ["contractid", "operationdate"]}], "writes": [{"table": "communitycourts", "columns": ["contractid", "operationdate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO circular_economy_companies SELECT warehouse_state, mental_health_status, likes FROM bi_campaigns_delta WHERE warehouse_state > 296\")\n", "labels": {"reads": [{"table": "bi_campaigns_delta", "columns": ["warehouse_state", "mental_health_status", "likes"]}], "writes": [{"table": "circular_economy_companies", "columns": ["warehouse_state", "mental_health_status", "likes"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nimport logging\nsql = \"INSERT INTO chip_model SELECT a.sale_quantity, b.hours_contributed FROM body_builder a JOIN multimodal_trips b ON a.restaurant = b.restaurant\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "body_builder", "columns": null}, {"table": "multimodal_trips", "columns": null}], "writes": [{"table": "chip_model", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO teacher_professional_development SELECT num_transactions, event_count, policy_count FROM atlantic_marine_life WHERE num_transactions > 400\");\n", "labels": {"reads": [{"table": "atlantic_marine_life", "columns": ["num_transactions", "event_count", "policy_count"]}], "writes": [{"table": "teacher_professional_development", "columns": ["num_transactions", "event_count", "policy_count"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"storage_projects\").toPandas()\ndf[[\"menu_item\", \"resolution\"]].to_sql(\"programs\", engine, index=False)\n", "labels": {"reads": [{"table": "storage_projects", "columns": null}], "writes": [{"table": "programs", "columns": ["menu_item", "resolution"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vessel_types SELECT representative_name, hourid, donor_program, date_in_locaton_to FROM biosensor.patents WHERE representative_name > 16\"\n", "labels": {"reads": [{"table": "biosensor.patents", "columns": ["representative_name", "hourid", "donor_program", "date_in_locaton_to"]}], "writes": [{"table": "vessel_types", "columns": ["representative_name", "hourid", "donor_program", "date_in_locaton_to"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table satellitedata --target-dir /tmp/land\n", "labels": {"reads": [{"table": "satellitedata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO dw.dw_sessions_delta SELECT practices, trial_id, response_received_date, undergraduate FROM artworksales WHERE practices > 68\")\n", "labels": {"reads": [{"table": "artworksales", "columns": ["practices", "trial_id", "response_received_date", "undergraduate"]}], "writes": [{"table": "dw.dw_sessions_delta", "columns": ["practices", "trial_id", "response_received_date", "undergraduate"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO recreation_centers SELECT * FROM legacy\nspark.sql(\"INSERT INTO digital_trends SELECT financial_capability_score, incident_region FROM philadelphia_police_emergencies WHERE financial_capability_score > 191\")\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": ["financial_capability_score", "incident_region"]}], "writes": [{"table": "digital_trends", "columns": ["financial_capability_score", "incident_region"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads.ads_shipments_delta SELECT a.restaurantid, b.contributionid FROM regulatory_frameworks a JOIN peacekeepingmissions b ON a.trainingyear = b.trainingyear\"\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": null}, {"table": "peacekeepingmissions", "columns": null}], "writes": [{"table": "ads.ads_shipments_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table activities --target-dir /tmp/land\n", "labels": {"reads": [{"table": "activities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO online_platform SELECT a.menucategory, b.grant_type FROM decentralized_applications a JOIN mart.coupon_use_hourly b ON a.provider_name = b.provider_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "decentralized_applications", "columns": null}, {"table": "mart.coupon_use_hourly", "columns": null}], "writes": [{"table": "online_platform", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO courtcases SELECT height, fate FROM diplomacy_events WHERE height > 343\");\n", "labels": {"reads": [{"table": "diplomacy_events", "columns": ["height", "fate"]}], "writes": [{"table": "courtcases", "columns": ["height", "fate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO smart_contracts_transactions SELECT country_code, sensor_id, end_date FROM salinity_readings WHERE country_code > 154\")\n", "labels": {"reads": [{"table": "salinity_readings", "columns": ["country_code", "sensor_id", "end_date"]}], "writes": [{"table": "smart_contracts_transactions", "columns": ["country_code", "sensor_id", "end_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO landfill_capacity_city_v2 SELECT * FROM legacy\ncur.execute(\"SELECT patientid, patient_id FROM waterusage LIMIT 341\")\n", "labels": {"reads": [{"table": "waterusage", "columns": ["patientid", "patient_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO store_district SELECT attorney_id, museum_id FROM farm WHERE attorney_id > 210\")\n", "labels": {"reads": [{"table": "farm", "columns": ["attorney_id", "museum_id"]}], "writes": [{"table": "store_district", "columns": ["attorney_id", "museum_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO album SELECT event, comptroller, state FROM donation WHERE event > 350\"], check=True)\n", "labels": {"reads": [{"table": "donation", "columns": ["event", "comptroller", "state"]}], "writes": [{"table": "album", "columns": ["event", "comptroller", "state"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM deep_sea_expeditions\"\n", "labels": {"reads": [{"table": "deep_sea_expeditions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"open_pedagogy_exam\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dwd.dwd_exposure_df\")\n", "labels": {"reads": [{"table": "open_pedagogy_exam", "columns": null}], "writes": [{"table": "dwd.dwd_exposure_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"cosmetics\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "cosmetics", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO fans_merchandise_basketball SELECT numhearings, contractor_name, support_id FROM endowment WHERE numhearings > 497\");\n", "labels": {"reads": [{"table": "endowment", "columns": ["numhearings", "contractor_name", "support_id"]}], "writes": [{"table": "fans_merchandise_basketball", "columns": ["numhearings", "contractor_name", "support_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_device_log_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"accounts\")\n", "labels": {"reads": [{"table": "stg.stg_device_log_daily", "columns": null}], "writes": [{"table": "accounts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dailystreams depends on bi.bi_inventory\ndbt build --select dailystreams --vars '{\"source_table\":\"bi.bi_inventory\"}'\n", "labels": {"reads": [{"table": "bi.bi_inventory", "columns": null}], "writes": [{"table": "dailystreams", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO donations SELECT book_id, date_in_locaton_to, gender_group FROM agricultural_innovation WHERE book_id > 379\"], check=True)\n", "labels": {"reads": [{"table": "agricultural_innovation", "columns": ["book_id", "date_in_locaton_to", "gender_group"]}], "writes": [{"table": "donations", "columns": ["book_id", "date_in_locaton_to", "gender_group"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO dws_products SELECT product_details, visitor_count FROM dysprosium_mines WHERE product_details > 17\");\n", "labels": {"reads": [{"table": "dysprosium_mines", "columns": ["product_details", "visitor_count"]}], "writes": [{"table": "dws_products", "columns": ["product_details", "visitor_count"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 206;\nSQL\n", "labels": {"reads": [{"table": "sustainable_urban_properties_2", "columns": ["advisoryid", "unionname"]}, {"table": "deep_sea_expeditions", "columns": ["iata", "training_name"]}], "writes": [{"table": "ads.refunds_delta", "columns": ["iata", "training_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO climate_finance_organizations (communityid, case_burden) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "climate_finance_organizations", "columns": ["communityid", "case_burden"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM deliveryaddresses\"\n", "labels": {"reads": [{"table": "deliveryaddresses", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO bi.bi_inventory_di SELECT safety_score, home_city FROM threat_intel WHERE safety_score > 367\");\n", "labels": {"reads": [{"table": "threat_intel", "columns": ["safety_score", "home_city"]}], "writes": [{"table": "bi.bi_inventory_di", "columns": ["safety_score", "home_city"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fertilizer_usage SELECT site_name, nominee, membership_amount FROM bi.users_full WHERE site_name > 250\"\n", "labels": {"reads": [{"table": "bi.users_full", "columns": ["site_name", "nominee", "membership_amount"]}], "writes": [{"table": "fertilizer_usage", "columns": ["site_name", "nominee", "membership_amount"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT name_last, sculpture_name FROM climate_finance_organizations LIMIT 20\")\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO chemicals SELECT menu_item_id, inclusivehousing, participant_count FROM sanctuaryanimals WHERE menu_item_id > 264\")\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": ["name_last", "sculpture_name"]}, {"table": "sanctuaryanimals", "columns": ["menu_item_id", "inclusivehousing", "participant_count"]}], "writes": [{"table": "chemicals", "columns": ["menu_item_id", "inclusivehousing", "participant_count"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO hospitallocations SELECT student_capacity, all_games FROM digital_assets WHERE student_capacity > 330\"], check=True)\n", "labels": {"reads": [{"table": "digital_assets", "columns": ["student_capacity", "all_games"]}], "writes": [{"table": "hospitallocations", "columns": ["student_capacity", "all_games"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM sfc_articles\"\n", "labels": {"reads": [{"table": "sfc_articles", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO trust SELECT booked_count, decoration_theme, fieldid FROM space_missions_2 WHERE booked_count > 261\"\n", "labels": {"reads": [{"table": "space_missions_2", "columns": ["booked_count", "decoration_theme", "fieldid"]}], "writes": [{"table": "trust", "columns": ["booked_count", "decoration_theme", "fieldid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO council_tax SELECT casestatus, representative_id FROM recycledmaterialsgarments WHERE casestatus > 181\");\n", "labels": {"reads": [{"table": "recycledmaterialsgarments", "columns": ["casestatus", "representative_id"]}], "writes": [{"table": "council_tax", "columns": ["casestatus", "representative_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO songs_length SELECT continent, monthly_rental, farmid, minutes FROM programs WHERE continent > 91\"\n", "labels": {"reads": [{"table": "programs", "columns": ["continent", "monthly_rental", "farmid", "minutes"]}], "writes": [{"table": "songs_length", "columns": ["continent", "monthly_rental", "farmid", "minutes"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"food_justice\").where(\"dt = current_date()\").writeTo(\"textileworkers\").append()\n", "labels": {"reads": [{"table": "food_justice", "columns": null}], "writes": [{"table": "textileworkers", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO trenches SELECT contract_type, province_id, tech FROM midwest_region WHERE contract_type > 410\")\n", "labels": {"reads": [{"table": "midwest_region", "columns": ["contract_type", "province_id", "tech"]}], "writes": [{"table": "trenches", "columns": ["contract_type", "province_id", "tech"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO traveler SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO euroavev SELECT a.trainingtype, b.long FROM genetics_stats.research_projects a JOIN educators b ON a.donor_country = b.donor_country\"\n", "labels": {"reads": [{"table": "genetics_stats.research_projects", "columns": null}, {"table": "educators", "columns": null}], "writes": [{"table": "euroavev", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO singer_in_concert SELECT creator, closuredate, issued_date FROM host WHERE creator > 401\");\n", "labels": {"reads": [{"table": "host", "columns": ["creator", "closuredate", "issued_date"]}], "writes": [{"table": "singer_in_concert", "columns": ["creator", "closuredate", "issued_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO safetytestingcounts SELECT bus_id, line_1_number_building, purchase_id, class_president_vote FROM projecttimeline WHERE bus_id > 235\"\n", "labels": {"reads": [{"table": "projecttimeline", "columns": ["bus_id", "line_1_number_building", "purchase_id", "class_president_vote"]}], "writes": [{"table": "safetytestingcounts", "columns": ["bus_id", "line_1_number_building", "purchase_id", "class_president_vote"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cycling SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO territory.human_rights_data (num_songs, cause) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "territory.human_rights_data", "columns": ["num_songs", "cause"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 268;\nEOF\n", "labels": {"reads": [{"table": "experience", "columns": ["transact_count", "shariah_compliant_investment_amount"]}], "writes": [{"table": "mart.mart_device_log_hourly", "columns": ["transact_count", "shariah_compliant_investment_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO sustainableproduction SELECT a.doctorsper1000, b.party FROM recycling_centers a JOIN uel_top10 b ON a.wage_increase = b.wage_increase\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "recycling_centers", "columns": null}, {"table": "uel_top10", "columns": null}], "writes": [{"table": "sustainableproduction", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"financial_capability_programs\")\nsrc.write.insertInto(\"refugees\", overwrite=True)\n", "labels": {"reads": [{"table": "financial_capability_programs", "columns": null}], "writes": [{"table": "refugees", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 66;\nEOF\n", "labels": {"reads": [{"table": "channel", "columns": ["did", "precedent_id", "total_beds", "end_station_name"]}], "writes": [{"table": "trenches", "columns": ["did", "precedent_id", "total_beds", "end_station_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO publicchargingstations SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT quality_rank, chair_name FROM ads_payments_hourly LIMIT 410\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": ["quality_rank", "chair_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.incident_type > 288).all()\n# src table: stg.stg_products_delta\nengine.execute(\"INSERT INTO route_fares SELECT * FROM stg.stg_products_delta\")\n", "labels": {"reads": [{"table": "stg.stg_products_delta", "columns": null}], "writes": [{"table": "route_fares", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 404;\nSQL\n", "labels": {"reads": [{"table": "permit", "columns": ["excavation_site_id", "organic_ingredients_percentage"]}, {"table": "ads.ads_products_full", "columns": ["resource_id", "trip_type", "casetype", "organic"]}], "writes": [{"table": "band", "columns": ["resource_id", "trip_type", "casetype", "organic"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM funding_rounds\"\n", "labels": {"reads": [{"table": "funding_rounds", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO policy_feedback SELECT exhibition_name, initiative_type, asset_disposed_date FROM circular_economy WHERE exhibition_name > 204\"\n", "labels": {"reads": [{"table": "circular_economy", "columns": ["exhibition_name", "initiative_type", "asset_disposed_date"]}], "writes": [{"table": "policy_feedback", "columns": ["exhibition_name", "initiative_type", "asset_disposed_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 72;\nEOF\n", "labels": {"reads": [{"table": "marine_species_status", "columns": ["headquarter", "org_size"]}], "writes": [{"table": "farmer_details", "columns": ["headquarter", "org_size"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.amount_waste > 170).all()\n# src table: textile_sourcing\nengine.execute(\"INSERT INTO space_missions SELECT * FROM textile_sourcing\")\n", "labels": {"reads": [{"table": "textile_sourcing", "columns": null}], "writes": [{"table": "space_missions", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT founder_count, agegroup FROM co_ownership\", engine)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"diversity_metrics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "co_ownership", "columns": ["founder_count", "agegroup"]}], "writes": [{"table": "diversity_metrics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO field5 SELECT email, phase, weeks_on_top FROM employees WHERE email > 12\")\n", "labels": {"reads": [{"table": "employees", "columns": ["email", "phase", "weeks_on_top"]}], "writes": [{"table": "field5", "columns": ["email", "phase", "weeks_on_top"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO hydro_power SELECT reviewscore, amount_of_refund FROM audience WHERE reviewscore > 245\"\n", "labels": {"reads": [{"table": "audience", "columns": ["reviewscore", "amount_of_refund"]}], "writes": [{"table": "hydro_power", "columns": ["reviewscore", "amount_of_refund"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"albums\").where(\"dt = current_date()\").writeTo(\"immunizationrates\").append()\n", "labels": {"reads": [{"table": "albums", "columns": null}], "writes": [{"table": "immunizationrates", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO purchases SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM seafoodsouthafricakenya\", conn)\ndf.to_sql(\"ads_payments_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "seafoodsouthafricakenya", "columns": null}], "writes": [{"table": "ads_payments_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT nid, name_first FROM professionals LIMIT 197\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO program_outcomes SELECT room_count, contributiondate FROM virtual_tour_revenue WHERE room_count > 229\")\n", "labels": {"reads": [{"table": "professionals", "columns": ["nid", "name_first"]}, {"table": "virtual_tour_revenue", "columns": ["room_count", "contributiondate"]}], "writes": [{"table": "program_outcomes", "columns": ["room_count", "contributiondate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO iron (framework_id, sale_year) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "iron", "columns": ["framework_id", "sale_year"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT socialimpactscore, to_address FROM military_expenditure\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"ods.ods_clicks_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "military_expenditure", "columns": ["socialimpactscore", "to_address"]}], "writes": [{"table": "ods.ods_clicks_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bus_routes\").where(\"dt = current_date()\").writeTo(\"mart.mart_users_delta\").append()\n", "labels": {"reads": [{"table": "bus_routes", "columns": null}], "writes": [{"table": "mart.mart_users_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model inclusive_housing depends on members\ndbt run -s inclusive_housing --vars 'source: members'\n", "labels": {"reads": [{"table": "members", "columns": null}], "writes": [{"table": "inclusive_housing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"properties\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stg.stg_products_delta\")\n", "labels": {"reads": [{"table": "properties", "columns": null}], "writes": [{"table": "stg.stg_products_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT policy_description, sellingprice FROM epl_teams LIMIT 372\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO gradeconversion SELECT pilot_id, total_points FROM residents_services WHERE pilot_id > 227\")\n", "labels": {"reads": [{"table": "epl_teams", "columns": ["policy_description", "sellingprice"]}, {"table": "residents_services", "columns": ["pilot_id", "total_points"]}], "writes": [{"table": "gradeconversion", "columns": ["pilot_id", "total_points"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 14;\nEOF\n", "labels": {"reads": [{"table": "ods.ods_sessions_df", "columns": ["policyname", "cases_count", "ad_id"]}], "writes": [{"table": "unionnegotiations", "columns": ["policyname", "cases_count", "ad_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO trafficviolations SELECT * FROM legacy\ncur.execute(\"SELECT job_title, numcases FROM pilot_record LIMIT 90\")\n", "labels": {"reads": [{"table": "pilot_record", "columns": ["job_title", "numcases"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO trains SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table mart.mart_campaigns_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table problem_log --target-dir /tmp/land\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT oil_production_q4_2021, claim_id FROM disaster_response_donations\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"ods.ods_device_log_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": ["oil_production_q4_2021", "claim_id"]}], "writes": [{"table": "ods.ods_device_log_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO maintenance_contracts SELECT a.itemid, b.institution FROM broadband_customers_global a JOIN wildlife_habitats b ON a.production_bopd = b.production_bopd\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": null}, {"table": "wildlife_habitats", "columns": null}], "writes": [{"table": "maintenance_contracts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO impact_investments SELECT team_id, engineer_id, other_item_details, num_tools FROM dwd.dwd_vendors WHERE team_id > 249\");\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": ["team_id", "engineer_id", "other_item_details", "num_tools"]}], "writes": [{"table": "impact_investments", "columns": ["team_id", "engineer_id", "other_item_details", "num_tools"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"cargo_data\").where(\"dt = current_date()\").writeTo(\"circular_economy_initiatives\").append()\n", "labels": {"reads": [{"table": "cargo_data", "columns": null}], "writes": [{"table": "circular_economy_initiatives", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contract_address, artworkid FROM fairtradecertification LIMIT 267\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO eco_hotels SELECT material_id, apt_number, enzyme_id, connection FROM african_tourism WHERE material_id > 307\")\n", "labels": {"reads": [{"table": "fairtradecertification", "columns": ["contract_address", "artworkid"]}, {"table": "african_tourism", "columns": ["material_id", "apt_number", "enzyme_id", "connection"]}], "writes": [{"table": "eco_hotels", "columns": ["material_id", "apt_number", "enzyme_id", "connection"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table galleryc --target-dir /tmp/land\n", "labels": {"reads": [{"table": "galleryc", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO global_sales_2022 SELECT institution_id, invoice_date, business_id FROM maintenancerequests WHERE institution_id > 150\")\n", "labels": {"reads": [{"table": "maintenancerequests", "columns": ["institution_id", "invoice_date", "business_id"]}], "writes": [{"table": "global_sales_2022", "columns": ["institution_id", "invoice_date", "business_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"wrestler\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "wrestler", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table humanitarianassistanceoperations --columns semester,contract_end --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "humanitarianassistanceoperations", "columns": ["semester", "contract_end"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"nailpolishsales\").where(\"dt = current_date()\").writeTo(\"autonomousdriving\").append()\n", "labels": {"reads": [{"table": "nailpolishsales", "columns": null}], "writes": [{"table": "autonomousdriving", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO album SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO publicchargingstations SELECT meal_id, organized_by FROM hotel_ratings WHERE meal_id > 36\"], check=True)\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": ["meal_id", "organized_by"]}], "writes": [{"table": "publicchargingstations", "columns": ["meal_id", "organized_by"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO timber_production SELECT a.complaint_id, b.production_qty FROM menu a JOIN communitypolicingcenters b ON a.eid = b.eid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "menu", "columns": null}, {"table": "communitypolicingcenters", "columns": null}], "writes": [{"table": "timber_production", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 390;\nEOF\n", "labels": {"reads": [{"table": "affordablehousing", "columns": ["dispensaryid", "menuid", "total_amount"]}], "writes": [{"table": "deep_sea_expeditions", "columns": ["dispensaryid", "menuid", "total_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT devices, certification_id FROM device LIMIT 187\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO ods_payments_delta SELECT enable_third_party_ads, union_members FROM job_postings WHERE enable_third_party_ads > 56\")\n", "labels": {"reads": [{"table": "device", "columns": ["devices", "certification_id"]}, {"table": "job_postings", "columns": ["enable_third_party_ads", "union_members"]}], "writes": [{"table": "ods_payments_delta", "columns": ["enable_third_party_ads", "union_members"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO authors SELECT monthly_rental, apt_id, organic_matter FROM acidification_data WHERE monthly_rental > 404\"\n", "labels": {"reads": [{"table": "acidification_data", "columns": ["monthly_rental", "apt_id", "organic_matter"]}], "writes": [{"table": "authors", "columns": ["monthly_rental", "apt_id", "organic_matter"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO exhibition_record SELECT * FROM legacy\nspark.sql(\"INSERT INTO rooms SELECT home_team_id, dphone, technique, seat_section FROM ocean_depths WHERE home_team_id > 298\")\n", "labels": {"reads": [{"table": "ocean_depths", "columns": ["home_team_id", "dphone", "technique", "seat_section"]}], "writes": [{"table": "rooms", "columns": ["home_team_id", "dphone", "technique", "seat_section"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO transportation_per_country SELECT policy_type, region_name, entrydate, volunteer_hours FROM disabilitysupportprograms WHERE policy_type > 294\"\n", "labels": {"reads": [{"table": "disabilitysupportprograms", "columns": ["policy_type", "region_name", "entrydate", "volunteer_hours"]}], "writes": [{"table": "transportation_per_country", "columns": ["policy_type", "region_name", "entrydate", "volunteer_hours"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO nutritionfacts SELECT opponent_id, owner, accreditation_type, cruelty_free FROM user_video_view WHERE opponent_id > 111\")\n", "labels": {"reads": [{"table": "user_video_view", "columns": ["opponent_id", "owner", "accreditation_type", "cruelty_free"]}], "writes": [{"table": "nutritionfacts", "columns": ["opponent_id", "owner", "accreditation_type", "cruelty_free"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.port > 448).all()\n# src table: safetyorgs\nengine.execute(\"INSERT INTO manufacturersustainability SELECT * FROM safetyorgs\")\n", "labels": {"reads": [{"table": "safetyorgs", "columns": null}], "writes": [{"table": "manufacturersustainability", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"satellitematerials\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"aircraftsquadrons\")\n", "labels": {"reads": [{"table": "satellitematerials", "columns": null}], "writes": [{"table": "aircraftsquadrons", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT destroyed_by_employee_id, quantity_sold FROM causes LIMIT 405\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO arctic_research SELECT cinema_id, vegetable, co2_reduction_tons, emp_lname FROM financial_capability_program WHERE cinema_id > 69\")\n", "labels": {"reads": [{"table": "causes", "columns": ["destroyed_by_employee_id", "quantity_sold"]}, {"table": "financial_capability_program", "columns": ["cinema_id", "vegetable", "co2_reduction_tons", "emp_lname"]}], "writes": [{"table": "arctic_research", "columns": ["cinema_id", "vegetable", "co2_reduction_tons", "emp_lname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ref_shipping_agents\");\ndf.write().mode(\"overwrite\").saveAsTable(\"activity\");\n", "labels": {"reads": [{"table": "ref_shipping_agents", "columns": null}], "writes": [{"table": "activity", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO mining_companies SELECT genre_is, patientid, policyid FROM bridges WHERE genre_is > 201\");\n", "labels": {"reads": [{"table": "bridges", "columns": ["genre_is", "patientid", "policyid"]}], "writes": [{"table": "mining_companies", "columns": ["genre_is", "patientid", "policyid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_incident_end, incident_date FROM mine LIMIT 329\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [{"table": "mine", "columns": ["date_incident_end", "incident_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"weekly_weather\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "weekly_weather", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO carbon_footprint SELECT hourid, maintenancedate, wage_increase, trip_date FROM campuses WHERE hourid > 374\"\n", "labels": {"reads": [{"table": "campuses", "columns": ["hourid", "maintenancedate", "wage_increase", "trip_date"]}], "writes": [{"table": "carbon_footprint", "columns": ["hourid", "maintenancedate", "wage_increase", "trip_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"inventory\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "inventory", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"regulatoryframeworksbycountry\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"student_access\")\n", "labels": {"reads": [{"table": "regulatoryframeworksbycountry", "columns": null}], "writes": [{"table": "student_access", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO weather_record SELECT publication_id, shelter_name FROM project_issues WHERE publication_id > 214\");\n", "labels": {"reads": [{"table": "project_issues", "columns": ["publication_id", "shelter_name"]}], "writes": [{"table": "weather_record", "columns": ["publication_id", "shelter_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO efforts SELECT performancedate, sensor_type, production_date FROM inventory WHERE performancedate > 71\"\n", "labels": {"reads": [{"table": "inventory", "columns": ["performancedate", "sensor_type", "production_date"]}], "writes": [{"table": "efforts", "columns": ["performancedate", "sensor_type", "production_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO european_healthcare SELECT birthdate, participant_count, negative FROM mental_health_center WHERE birthdate > 91\"\n", "labels": {"reads": [{"table": "mental_health_center", "columns": ["birthdate", "participant_count", "negative"]}], "writes": [{"table": "european_healthcare", "columns": ["birthdate", "participant_count", "negative"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO recreation_centers SELECT a.installed_date, b.crime_id FROM book a JOIN diplomacy_events b ON a.sculpture_name = b.sculpture_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "book", "columns": null}, {"table": "diplomacy_events", "columns": null}], "writes": [{"table": "recreation_centers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO phishing_attempts SELECT a.price_in_euros, b.dish_name FROM drills a JOIN workercontactinfo b ON a.tech_id = b.tech_id\"\n", "labels": {"reads": [{"table": "drills", "columns": null}, {"table": "workercontactinfo", "columns": null}], "writes": [{"table": "phishing_attempts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"humanitarianmissions\").where(\"dt = current_date()\").writeTo(\"participation\").append()\n", "labels": {"reads": [{"table": "humanitarianmissions", "columns": null}], "writes": [{"table": "participation", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_table(ctx, \"genre_songs\")\nwrite_to_sink(df, \"immunization\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "genre_songs", "columns": null}], "writes": [{"table": "immunization", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table zip_codes --columns client_name,vrgameid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "zip_codes", "columns": ["client_name", "vrgameid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO fair_trade_suppliers SELECT views, group_name, defense_contractor_id, lesson_status_code FROM renewable_projects WHERE views > 457\")\n", "labels": {"reads": [{"table": "renewable_projects", "columns": ["views", "group_name", "defense_contractor_id", "lesson_status_code"]}], "writes": [{"table": "fair_trade_suppliers", "columns": ["views", "group_name", "defense_contractor_id", "lesson_status_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 115;\nEOF\n", "labels": {"reads": [{"table": "sportsinfo", "columns": ["amount_of_transaction", "actual_delivery_date", "category_id", "reviewscore"]}], "writes": [{"table": "vehicle", "columns": ["amount_of_transaction", "actual_delivery_date", "category_id", "reviewscore"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"machine_emissions\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws.dws_coupon_use_di\");\n", "labels": {"reads": [{"table": "machine_emissions", "columns": null}], "writes": [{"table": "dws.dws_coupon_use_di", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO city_waste_generation SELECT crop_name, retailer FROM ads.ads_products_full WHERE crop_name > 86\"\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": ["crop_name", "retailer"]}], "writes": [{"table": "city_waste_generation", "columns": ["crop_name", "retailer"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO energy_prices SELECT fiscal_year, maintenance_id, lawyer_name, artwork_id FROM player_f WHERE fiscal_year > 249\")\n", "labels": {"reads": [{"table": "player_f", "columns": ["fiscal_year", "maintenance_id", "lawyer_name", "artwork_id"]}], "writes": [{"table": "energy_prices", "columns": ["fiscal_year", "maintenance_id", "lawyer_name", "artwork_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO jupiter_spacecraft SELECT a.practice, b.claim_outcome_code FROM foodsafetyrecords a JOIN management b ON a.product_category_description = b.product_category_description\"\n", "labels": {"reads": [{"table": "foodsafetyrecords", "columns": null}, {"table": "management", "columns": null}], "writes": [{"table": "jupiter_spacecraft", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO volunteer_signups SELECT annual_entry_exit, users_engaged, labor_practice, subscribe_date FROM phishing_targets WHERE annual_entry_exit > 343\"\n", "labels": {"reads": [{"table": "phishing_targets", "columns": ["annual_entry_exit", "users_engaged", "labor_practice", "subscribe_date"]}], "writes": [{"table": "volunteer_signups", "columns": ["annual_entry_exit", "users_engaged", "labor_practice", "subscribe_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO digital_divide_initiatives SELECT transaction_product, mine_id FROM category_revenue WHERE transaction_product > 429\"\n", "labels": {"reads": [{"table": "category_revenue", "columns": ["transaction_product", "mine_id"]}], "writes": [{"table": "digital_divide_initiatives", "columns": ["transaction_product", "mine_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 480;\nEOF\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": ["line_1_number_building", "store_id", "scientist", "accident_date"]}], "writes": [{"table": "material_production", "columns": ["line_1_number_building", "store_id", "scientist", "accident_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"philadelphia_police_emergencies\");\ndf.write().mode(\"overwrite\").saveAsTable(\"public.collected_fare\");\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": null}], "writes": [{"table": "public.collected_fare", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table heritage_sites_3 --columns location_id,school_colors --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "heritage_sites_3", "columns": ["location_id", "school_colors"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table royal_family --target-dir /tmp/land\n", "labels": {"reads": [{"table": "royal_family", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO employment SELECT contractorid, matchdate FROM charging_stations WHERE contractorid > 386\"\n", "labels": {"reads": [{"table": "charging_stations", "columns": ["contractorid", "matchdate"]}], "writes": [{"table": "employment", "columns": ["contractorid", "matchdate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cosmetics SELECT last_checkup_date, market_value, form_type_code FROM mart_refunds_delta WHERE last_checkup_date > 134\"\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": ["last_checkup_date", "market_value", "form_type_code"]}], "writes": [{"table": "cosmetics", "columns": ["last_checkup_date", "market_value", "form_type_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 359;\nSQL\n", "labels": {"reads": [{"table": "cosmetics_sales", "columns": ["station_name", "movieid"]}, {"table": "fish_biomass", "columns": ["participant_name", "product_subcategory", "ngo_name"]}], "writes": [{"table": "communitydevelopment", "columns": ["participant_name", "product_subcategory", "ngo_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"benefits_overpayments\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"co2_sequestration\")\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": null}], "writes": [{"table": "co2_sequestration", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 20;\nEOF\n", "labels": {"reads": [{"table": "recycling_centers", "columns": ["drugname", "ssn"]}], "writes": [{"table": "food_safety_inspections", "columns": ["drugname", "ssn"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"climate_data\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ods.shipments_df\")\n", "labels": {"reads": [{"table": "climate_data", "columns": null}], "writes": [{"table": "ods.shipments_df", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"employees\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"wastedata\")\n", "labels": {"reads": [{"table": "employees", "columns": null}], "writes": [{"table": "wastedata", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO criminalcases SELECT * FROM legacy\nspark.sql(\"INSERT INTO smart_contracts_table SELECT total_distance, date_left_staff FROM musicgenre WHERE total_distance > 106\")\n", "labels": {"reads": [{"table": "musicgenre", "columns": ["total_distance", "date_left_staff"]}], "writes": [{"table": "smart_contracts_table", "columns": ["total_distance", "date_left_staff"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT starting_year, num_stops FROM genre_songs LIMIT 487\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "genre_songs", "columns": ["starting_year", "num_stops"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"africa_schema.african_mines\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"domesticconferences\")\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": null}], "writes": [{"table": "domesticconferences", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT time_hour, ll_id FROM menu_items LIMIT 40\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "menu_items", "columns": ["time_hour", "ll_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table dwd.vendors --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dwd.vendors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"train_lines\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"investor\")\n", "labels": {"reads": [{"table": "train_lines", "columns": null}], "writes": [{"table": "investor", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"world_heritage_sites\").where(\"dt = current_date()\").writeTo(\"film\").append()\n", "labels": {"reads": [{"table": "world_heritage_sites", "columns": null}], "writes": [{"table": "film", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi.users_full\")\nsrc.write.insertInto(\"dws.exposure_df\", overwrite=True)\n", "labels": {"reads": [{"table": "bi.users_full", "columns": null}], "writes": [{"table": "dws.exposure_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO rebounds (museumname, route_short_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "rebounds", "columns": ["museumname", "route_short_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO goals SELECT departmentname, farmname, vehicle_flight_number, decision FROM tencel_sources WHERE departmentname > 386\");\n", "labels": {"reads": [{"table": "tencel_sources", "columns": ["departmentname", "farmname", "vehicle_flight_number", "decision"]}], "writes": [{"table": "goals", "columns": ["departmentname", "farmname", "vehicle_flight_number", "decision"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dst_apid, date_in_locaton_to FROM device_usage LIMIT 376\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "device_usage", "columns": ["dst_apid", "date_in_locaton_to"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.total_amount > 178).all()\n# src table: dws.inventory_df\nengine.execute(\"INSERT INTO ods.ods_campaigns_hourly SELECT * FROM dws.inventory_df\")\n", "labels": {"reads": [{"table": "dws.inventory_df", "columns": null}], "writes": [{"table": "ods.ods_campaigns_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO members SELECT society, dst_apid, production_bopd FROM virtual_tour_revenue WHERE society > 392\");\n", "labels": {"reads": [{"table": "virtual_tour_revenue", "columns": ["society", "dst_apid", "production_bopd"]}], "writes": [{"table": "members", "columns": ["society", "dst_apid", "production_bopd"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"investment_rounds\");\ndf.write().mode(\"overwrite\").saveAsTable(\"sustainableprojects\");\n", "labels": {"reads": [{"table": "investment_rounds", "columns": null}], "writes": [{"table": "sustainableprojects", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ref_incident_type SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO atlantic_marine_life SELECT train_id, supplierid, strain_name FROM weather_record WHERE train_id > 357\")\n", "labels": {"reads": [{"table": "weather_record", "columns": ["train_id", "supplierid", "strain_name"]}], "writes": [{"table": "atlantic_marine_life", "columns": ["train_id", "supplierid", "strain_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO streams SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 342;\nEOF\n", "labels": {"reads": [{"table": "innovation_grants", "columns": ["license_plate", "healthcareid", "site", "causename"]}], "writes": [{"table": "donation", "columns": ["license_plate", "healthcareid", "site", "causename"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.coupon_use SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibitiondetails\").toPandas()\ndf[[\"transaction_value\", \"contractor\"]].to_sql(\"cultural_competency_training\", engine, index=False)\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": null}], "writes": [{"table": "cultural_competency_training", "columns": ["transaction_value", "contractor"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO innovation_metrics SELECT manufacturername, booked_amount, grantid, undergraduate FROM caribbeansea WHERE manufacturername > 305\")\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["manufacturername", "booked_amount", "grantid", "undergraduate"]}], "writes": [{"table": "innovation_metrics", "columns": ["manufacturername", "booked_amount", "grantid", "undergraduate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO workersalaries SELECT sustainability_initiative_id, labor_cost FROM dwd.dwd_vendors WHERE sustainability_initiative_id > 281\");\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": ["sustainability_initiative_id", "labor_cost"]}], "writes": [{"table": "workersalaries", "columns": ["sustainability_initiative_id", "labor_cost"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO provinces (emp_jobcode, occupation) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "provinces", "columns": ["emp_jobcode", "occupation"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT price_in_dollars, primary FROM project_timelines\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"restorative_justice_3\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "project_timelines", "columns": ["price_in_dollars", "primary"]}], "writes": [{"table": "restorative_justice_3", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"ads.refunds_delta\")\nsrc.write.insertInto(\"gamegenres\", overwrite=True)\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": null}], "writes": [{"table": "gamegenres", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ods_events_daily depends on matches\ndbt build --select ods_events_daily --vars 'source: matches'\n", "labels": {"reads": [{"table": "matches", "columns": null}], "writes": [{"table": "ods_events_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dwd.inventory_df SELECT investors, asset_type, classroom, digital FROM infrastructureprojects WHERE investors > 252\"\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": ["investors", "asset_type", "classroom", "digital"]}], "writes": [{"table": "dwd.inventory_df", "columns": ["investors", "asset_type", "classroom", "digital"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table user_video_view --columns sales_details,inspectionid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "user_video_view", "columns": ["sales_details", "inspectionid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dwd_sessions_hourly SELECT max_dew_point_f, fundingid, aircraft_id, assistingnurse FROM inspectiondata WHERE max_dew_point_f > 445\"\n", "labels": {"reads": [{"table": "inspectiondata", "columns": ["max_dew_point_f", "fundingid", "aircraft_id", "assistingnurse"]}], "writes": [{"table": "dwd_sessions_hourly", "columns": ["max_dew_point_f", "fundingid", "aircraft_id", "assistingnurse"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"recycling_centers\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "recycling_centers", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO energy_prices SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO warehouses SELECT a.contract_address, b.cmi_cross_ref_id FROM budget_allocations a JOIN yoga b ON a.inspectionid = b.inspectionid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}, {"table": "yoga", "columns": null}], "writes": [{"table": "warehouses", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table ai_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ai_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"threats\")\npush_to_store(df, \"ods_products_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "threats", "columns": null}], "writes": [{"table": "ods_products_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthparityscores\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"pacific_ocean\")\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": null}], "writes": [{"table": "pacific_ocean", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM aquaticfarm\"\n", "labels": {"reads": [{"table": "aquaticfarm", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT impact_score, product_details FROM rating LIMIT 424\")\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO race_ethnicity SELECT fan_age, manufacturerid FROM drills WHERE fan_age > 244\")\n", "labels": {"reads": [{"table": "rating", "columns": ["impact_score", "product_details"]}, {"table": "drills", "columns": ["fan_age", "manufacturerid"]}], "writes": [{"table": "race_ethnicity", "columns": ["fan_age", "manufacturerid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.clicks (artwork, tournament_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.clicks", "columns": ["artwork", "tournament_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.deliveryid > 480).all()\n# src table: virtual_visitors\nengine.execute(\"INSERT INTO athlete_stats SELECT * FROM virtual_visitors\")\n", "labels": {"reads": [{"table": "virtual_visitors", "columns": null}], "writes": [{"table": "athlete_stats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM satellites_by_country\", conn)\ndf.to_sql(\"musical\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": null}], "writes": [{"table": "musical", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO student_addresses SELECT season_number, dispensary FROM venture WHERE season_number > 57\");\n", "labels": {"reads": [{"table": "venture", "columns": ["season_number", "dispensary"]}], "writes": [{"table": "student_addresses", "columns": ["season_number", "dispensary"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vehicledata SELECT minesite, fish_count, social_impact_score, salesperson FROM virtual_tour_revenue WHERE minesite > 305\"\n", "labels": {"reads": [{"table": "virtual_tour_revenue", "columns": ["minesite", "fish_count", "social_impact_score", "salesperson"]}], "writes": [{"table": "vehicledata", "columns": ["minesite", "fish_count", "social_impact_score", "salesperson"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO emerging_markets.digital_assets SELECT follow_up_date, intervention_type, storeid, approval_date FROM packages WHERE follow_up_date > 452\"\n", "labels": {"reads": [{"table": "packages", "columns": ["follow_up_date", "intervention_type", "storeid", "approval_date"]}], "writes": [{"table": "emerging_markets.digital_assets", "columns": ["follow_up_date", "intervention_type", "storeid", "approval_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO mart.mart_coupon_use_full SELECT institution, refugee_id, strat_name, company_type_code FROM cultural_competency WHERE institution > 347\")\n", "labels": {"reads": [{"table": "cultural_competency", "columns": ["institution", "refugee_id", "strat_name", "company_type_code"]}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": ["institution", "refugee_id", "strat_name", "company_type_code"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"menu\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_users_hourly\")\n", "labels": {"reads": [{"table": "menu", "columns": null}], "writes": [{"table": "dws.dws_users_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods.ods_risk_score_df SELECT total_spent, biome_id, domestic_passengers, trainingname FROM plays_games WHERE total_spent > 40\"\n", "labels": {"reads": [{"table": "plays_games", "columns": ["total_spent", "biome_id", "domestic_passengers", "trainingname"]}], "writes": [{"table": "ods.ods_risk_score_df", "columns": ["total_spent", "biome_id", "domestic_passengers", "trainingname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ai_systems\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "ai_systems", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM military_expenditure\", conn)\ndf.to_sql(\"economic_diversification_projects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "military_expenditure", "columns": null}], "writes": [{"table": "economic_diversification_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table beauty_products --columns providerid,model --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "beauty_products", "columns": ["providerid", "model"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT app_name, hours_served FROM dwd.dwd_campaigns\", engine)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"neodymium_prices\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns", "columns": ["app_name", "hours_served"]}], "writes": [{"table": "neodymium_prices", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO storage_tech SELECT totalprice, interaction_type FROM stg.stg_risk_score_df WHERE totalprice > 63\")\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": ["totalprice", "interaction_type"]}], "writes": [{"table": "storage_tech", "columns": ["totalprice", "interaction_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO shipment_data SELECT date_valid_from, patentexpirationdate, customer_code, attribute_name FROM ai_safety_papers2 WHERE date_valid_from > 447\"\n", "labels": {"reads": [{"table": "ai_safety_papers2", "columns": ["date_valid_from", "patentexpirationdate", "customer_code", "attribute_name"]}], "writes": [{"table": "shipment_data", "columns": ["date_valid_from", "patentexpirationdate", "customer_code", "attribute_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fleet\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"forests\")\n", "labels": {"reads": [{"table": "fleet", "columns": null}], "writes": [{"table": "forests", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"satellites_in_orbit\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"waste_management_projects\")\n", "labels": {"reads": [{"table": "satellites_in_orbit", "columns": null}], "writes": [{"table": "waste_management_projects", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO student_course_registrations SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.trip_type > 148).all()\n# src table: facility_production\nengine.execute(\"INSERT INTO continent SELECT * FROM facility_production\")\n", "labels": {"reads": [{"table": "facility_production", "columns": null}], "writes": [{"table": "continent", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO gymc_members SELECT transaction_date, address, funding_amount, job FROM city WHERE transaction_date > 58\")\n", "labels": {"reads": [{"table": "city", "columns": ["transaction_date", "address", "funding_amount", "job"]}], "writes": [{"table": "gymc_members", "columns": ["transaction_date", "address", "funding_amount", "job"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO retailers SELECT fan_id, field FROM communitypolicingcenters WHERE fan_id > 144\")\n", "labels": {"reads": [{"table": "communitypolicingcenters", "columns": ["fan_id", "field"]}], "writes": [{"table": "retailers", "columns": ["fan_id", "field"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table culturalevents --columns dept_name,attribute_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "culturalevents", "columns": ["dept_name", "attribute_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"team\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stores_2\")\n", "labels": {"reads": [{"table": "team", "columns": null}], "writes": [{"table": "stores_2", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO school_districts SELECT granteeid, tier FROM technology_access WHERE granteeid > 210\"\n", "labels": {"reads": [{"table": "technology_access", "columns": ["granteeid", "tier"]}], "writes": [{"table": "school_districts", "columns": ["granteeid", "tier"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO traffic SELECT is_vegetarian, itemname, num_shariah_compliant_investments, tier FROM clothingsales WHERE is_vegetarian > 285\")\n", "labels": {"reads": [{"table": "clothingsales", "columns": ["is_vegetarian", "itemname", "num_shariah_compliant_investments", "tier"]}], "writes": [{"table": "traffic", "columns": ["is_vegetarian", "itemname", "num_shariah_compliant_investments", "tier"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO underwater_trenches (flight_number, mean_temperature_f) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "underwater_trenches", "columns": ["flight_number", "mean_temperature_f"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table ocean_depths --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ocean_depths", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"astronauts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"industry_funding\")\n", "labels": {"reads": [{"table": "astronauts", "columns": null}], "writes": [{"table": "industry_funding", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"marine_species\");\ndf.write().mode(\"overwrite\").saveAsTable(\"teaches\");\n", "labels": {"reads": [{"table": "marine_species", "columns": null}], "writes": [{"table": "teaches", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vessel_name, subscriber_type FROM documents_to_be_destroyed\", engine)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"causes_insert_2\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "documents_to_be_destroyed", "columns": ["vessel_name", "subscriber_type"]}], "writes": [{"table": "causes_insert_2", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 61;\nEOF\n", "labels": {"reads": [{"table": "menuitems", "columns": ["dock_id", "funding_amount"]}], "writes": [{"table": "donations", "columns": ["dock_id", "funding_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_payments_delta\").toPandas()\ndf[[\"number_cities\", \"inspectionscore\"]].to_sql(\"hydro_power\", engine, index=False)\n", "labels": {"reads": [{"table": "ads.ads_payments_delta", "columns": null}], "writes": [{"table": "hydro_power", "columns": ["number_cities", "inspectionscore"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_input(ctx, \"player_college\")\ndump_to_store(df, \"attendance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "player_college", "columns": null}], "writes": [{"table": "attendance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 363;\nSQL\n", "labels": {"reads": [{"table": "military_innovation", "columns": ["yearadded", "openning_year"]}, {"table": "food_justice", "columns": ["co2_emission", "race", "disaster_type", "data_usage"]}], "writes": [{"table": "prescribes", "columns": ["co2_emission", "race", "disaster_type", "data_usage"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ods.ods_coupon_use_delta SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT check_in_id, event_id FROM hydro_plants\", engine)\nif not rows:\n logger.warning('empty result')\nimport logging\ndf.to_sql(\"reservoirs\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hydro_plants", "columns": ["check_in_id", "event_id"]}], "writes": [{"table": "reservoirs", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"investment_accounts\").toPandas()\ndf[[\"phone_number\", \"sensor_type\"]].to_sql(\"militarycyberops\", engine, index=False)\n", "labels": {"reads": [{"table": "investment_accounts", "columns": null}], "writes": [{"table": "militarycyberops", "columns": ["phone_number", "sensor_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi.bi_exposure_hourly (ship_id, amount_used) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_exposure_hourly", "columns": ["ship_id", "amount_used"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO team_franchise SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.mart_events_di\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mart.mart_events_di", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ods.ods_campaigns_df SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ethicalaibudget\");\ndf.write().mode(\"overwrite\").saveAsTable(\"claims_processing_stages\");\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": [{"table": "claims_processing_stages", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 147;\nEOF\n", "labels": {"reads": [{"table": "smart_contracts", "columns": ["transaction_type", "genre", "party_phone", "art_id"]}], "writes": [{"table": "eventlocations", "columns": ["transaction_type", "genre", "party_phone", "art_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"trade_history\");\ndf.write().mode(\"overwrite\").saveAsTable(\"sales_quarterly\");\n", "labels": {"reads": [{"table": "trade_history", "columns": null}], "writes": [{"table": "sales_quarterly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.meal_date > 398).all()\n# src table: dw.users_hourly\nengine.execute(\"INSERT INTO districts_india SELECT * FROM dw.users_hourly\")\n", "labels": {"reads": [{"table": "dw.users_hourly", "columns": null}], "writes": [{"table": "districts_india", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 489;\nEOF\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": ["exit_date", "museum_id", "max_temperature_f", "certification"]}], "writes": [{"table": "exhibition_visitors", "columns": ["exit_date", "museum_id", "max_temperature_f", "certification"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_inventory_di --columns characteristic_data_type,clicks --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_inventory_di", "columns": ["characteristic_data_type", "clicks"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainablebrands\").toPandas()\ndf[[\"budget_type_description\", \"rating_in_percent\"]].to_sql(\"dws.dws_risk_score_df\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": null}], "writes": [{"table": "dws.dws_risk_score_df", "columns": ["budget_type_description", "rating_in_percent"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tracks\").toPandas()\ndf[[\"high_estimate\", \"minister\"]].to_sql(\"election\", engine, index=False)\n", "labels": {"reads": [{"table": "tracks", "columns": null}], "writes": [{"table": "election", "columns": ["high_estimate", "minister"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donationhistory\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "donationhistory", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"innovation_projects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "innovation_projects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO investor_activities (galleryname, date_stored) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "investor_activities", "columns": ["galleryname", "date_stored"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO rating SELECT a.artifacttype, b.advisor FROM ads.member_point a JOIN bus_routes b ON a.hearingdate = b.hearingdate\"\n", "labels": {"reads": [{"table": "ads.member_point", "columns": null}, {"table": "bus_routes", "columns": null}], "writes": [{"table": "rating", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model police_calls depends on prison\ndbt build --models police_calls --vars 'source: prison'\n", "labels": {"reads": [{"table": "prison", "columns": null}], "writes": [{"table": "police_calls", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.attack_count > 124).all()\n# src table: oceanography\nengine.execute(\"INSERT INTO bi.events_df SELECT * FROM oceanography\")\n", "labels": {"reads": [{"table": "oceanography", "columns": null}], "writes": [{"table": "bi.events_df", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"machines\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"causes\")\n", "labels": {"reads": [{"table": "machines", "columns": null}], "writes": [{"table": "causes", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workforcediversity\").toPandas()\ndf[[\"common_name\", \"extraction_state\"]].to_sql(\"bi.bi_payments\", engine, index=False)\n", "labels": {"reads": [{"table": "workforcediversity", "columns": null}], "writes": [{"table": "bi.bi_payments", "columns": ["common_name", "extraction_state"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_cart_item\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws_cart_item", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO noise_pollution SELECT dysprosium_prod, impact_score, restaurant_name, vehicle_flight_number FROM wastewatertreatment WHERE dysprosium_prod > 281\"], check=True)\n", "labels": {"reads": [{"table": "wastewatertreatment", "columns": ["dysprosium_prod", "impact_score", "restaurant_name", "vehicle_flight_number"]}], "writes": [{"table": "noise_pollution", "columns": ["dysprosium_prod", "impact_score", "restaurant_name", "vehicle_flight_number"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bus_routes\"\n", "labels": {"reads": [{"table": "bus_routes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cargo_handling --columns quantity_containers,ngo_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cargo_handling", "columns": ["quantity_containers", "ngo_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT fundingamount, union_id FROM stg.stg_exposure_daily LIMIT 374\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO patient_satisfaction SELECT call_id, donation_date FROM ocean_health_monitor WHERE call_id > 453\")\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": ["fundingamount", "union_id"]}, {"table": "ocean_health_monitor", "columns": ["call_id", "donation_date"]}], "writes": [{"table": "patient_satisfaction", "columns": ["call_id", "donation_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"record\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "record", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"investment_strategies\");\ndf.write().mode(\"overwrite\").saveAsTable(\"prescribes\");\n", "labels": {"reads": [{"table": "investment_strategies", "columns": null}], "writes": [{"table": "prescribes", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cid, training_name FROM performingartsprograms LIMIT 244\")\nimport logging\nspark.sql(\"INSERT INTO patient_outcomes SELECT station_id, container_id, membership FROM fertilizer WHERE station_id > 429\")\n", "labels": {"reads": [{"table": "performingartsprograms", "columns": ["cid", "training_name"]}, {"table": "fertilizer", "columns": ["station_id", "container_id", "membership"]}], "writes": [{"table": "patient_outcomes", "columns": ["station_id", "container_id", "membership"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"product_reviews\").where(\"dt = current_date()\").writeTo(\"organizations\").append()\n", "labels": {"reads": [{"table": "product_reviews", "columns": null}], "writes": [{"table": "organizations", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dec, founded FROM competition LIMIT 431\")\nrows = cur.fetchall()\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "competition", "columns": ["dec", "founded"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"professor\")\nsrc.write.insertInto(\"causes\", overwrite=True)\n", "labels": {"reads": [{"table": "professor", "columns": null}], "writes": [{"table": "causes", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO crime_reports SELECT founding_date, workshop_id FROM revenue WHERE founding_date > 277\")\n", "labels": {"reads": [{"table": "revenue", "columns": ["founding_date", "workshop_id"]}], "writes": [{"table": "crime_reports", "columns": ["founding_date", "workshop_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO swimmer (min_salary, team) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "swimmer", "columns": ["min_salary", "team"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO marketing_budgets SELECT hometeam, emergency_type, room_number FROM mines WHERE hometeam > 216\");\n", "labels": {"reads": [{"table": "mines", "columns": ["hometeam", "emergency_type", "room_number"]}], "writes": [{"table": "marketing_budgets", "columns": ["hometeam", "emergency_type", "room_number"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table stg.stg_events_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.stg_events_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 248;\nEOF\n", "labels": {"reads": [{"table": "sportsinfo", "columns": ["date_problem_reported", "milestone", "language", "player"]}], "writes": [{"table": "courts", "columns": ["date_problem_reported", "milestone", "language", "player"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO wastewatertreatment SELECT company_type_code, login_name, emissions FROM public.crime_types WHERE company_type_code > 264\");\n", "labels": {"reads": [{"table": "public.crime_types", "columns": ["company_type_code", "login_name", "emissions"]}], "writes": [{"table": "wastewatertreatment", "columns": ["company_type_code", "login_name", "emissions"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO founder SELECT * FROM legacy\ncur.execute(\"SELECT line_1, product_type FROM dws.dws_inventory_di LIMIT 178\")\n", "labels": {"reads": [{"table": "dws.dws_inventory_di", "columns": ["line_1", "product_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO renewableenergy SELECT * FROM legacy\ncur.execute(\"SELECT custid, publish_date FROM russia_nato_diplomacy LIMIT 478\")\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": ["custid", "publish_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dw.dw_products_delta (status, mentalhealthscore) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_products_delta", "columns": ["status", "mentalhealthscore"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_inventory_full\"\n", "labels": {"reads": [{"table": "bi.bi_inventory_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO causes_insert_2 SELECT * FROM legacy\nspark.sql(\"INSERT INTO blockchain_tech SELECT problem_description, personnel_id, workouttype FROM dws.cart_item_full WHERE problem_description > 325\")\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": ["problem_description", "personnel_id", "workouttype"]}], "writes": [{"table": "blockchain_tech", "columns": ["problem_description", "personnel_id", "workouttype"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO disabilityadvocacy (assets_billion, issues) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "disabilityadvocacy", "columns": ["assets_billion", "issues"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"emerging_markets.digital_assets\")\nsink_to_store(df, \"baseball_teams\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "emerging_markets.digital_assets", "columns": null}], "writes": [{"table": "baseball_teams", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO minor_in SELECT material_id, festival_name, nurse, average FROM customers_cards WHERE material_id > 383\");\n", "labels": {"reads": [{"table": "customers_cards", "columns": ["material_id", "festival_name", "nurse", "average"]}], "writes": [{"table": "minor_in", "columns": ["material_id", "festival_name", "nurse", "average"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table mart.mart_member_point_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.mart_member_point_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO protein SELECT * FROM legacy\ncur.execute(\"SELECT regional_population, streamid FROM organisations LIMIT 91\")\n", "labels": {"reads": [{"table": "organisations", "columns": ["regional_population", "streamid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"contractorsales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "contractorsales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.services > 428).all()\n# src table: ods_clicks_df\nengine.execute(\"INSERT INTO spacemissions SELECT * FROM ods_clicks_df\")\n", "labels": {"reads": [{"table": "ods_clicks_df", "columns": null}], "writes": [{"table": "spacemissions", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table body_builder --columns subscribe_date,month --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "body_builder", "columns": ["subscribe_date", "month"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT amount_settled, artifact_type FROM restorative_justice_programs LIMIT 152\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO stops SELECT reviewscore, hardware_colours FROM customer WHERE reviewscore > 39\")\n", "labels": {"reads": [{"table": "restorative_justice_programs", "columns": ["amount_settled", "artifact_type"]}, {"table": "customer", "columns": ["reviewscore", "hardware_colours"]}], "writes": [{"table": "stops", "columns": ["reviewscore", "hardware_colours"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"yearly_production\");\ndf.write().mode(\"overwrite\").saveAsTable(\"mart.campaigns_full\");\n", "labels": {"reads": [{"table": "yearly_production", "columns": null}], "writes": [{"table": "mart.campaigns_full", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO department_publications SELECT * FROM legacy\ncur.execute(\"SELECT founded, trader_id FROM militarypatents LIMIT 22\")\n", "labels": {"reads": [{"table": "militarypatents", "columns": ["founded", "trader_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO communitydevelopment SELECT * FROM legacy\ncur.execute(\"SELECT order_item_status, role_code FROM higher_ed.students LIMIT 349\")\n", "labels": {"reads": [{"table": "higher_ed.students", "columns": ["order_item_status", "role_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM exhibitions\"\n", "labels": {"reads": [{"table": "exhibitions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO disabilitysupportprograms SELECT location_text, max_temperature_f, class_senator_vote FROM bi_refunds_daily WHERE location_text > 305\"\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["location_text", "max_temperature_f", "class_senator_vote"]}], "writes": [{"table": "disabilitysupportprograms", "columns": ["location_text", "max_temperature_f", "class_senator_vote"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO safety_incidents SELECT a.yield_id, b.medication FROM stg.stg_coupon_use_hourly a JOIN arcticocean b ON a.dock_id = b.dock_id\"\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_hourly", "columns": null}, {"table": "arcticocean", "columns": null}], "writes": [{"table": "safety_incidents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"bi.products_daily\")\nupsert_to_sink(df, \"stg.stg_exposure_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": null}], "writes": [{"table": "stg.stg_exposure_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO trafficviolations (publisher, transact_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "trafficviolations", "columns": ["publisher", "transact_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO customer_events SELECT units_sold, ingredient_name, production_date, suburb FROM train_lines WHERE units_sold > 271\"\n", "labels": {"reads": [{"table": "train_lines", "columns": ["units_sold", "ingredient_name", "production_date", "suburb"]}], "writes": [{"table": "customer_events", "columns": ["units_sold", "ingredient_name", "production_date", "suburb"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO stg_orders_hourly SELECT tour_type, eventattendance FROM safetytesting WHERE tour_type > 138\")\n", "labels": {"reads": [{"table": "safetytesting", "columns": ["tour_type", "eventattendance"]}], "writes": [{"table": "stg_orders_hourly", "columns": ["tour_type", "eventattendance"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"apartments\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"phone_market\")\n", "labels": {"reads": [{"table": "apartments", "columns": null}], "writes": [{"table": "phone_market", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mobile_plans\");\ndf.write().mode(\"overwrite\").saveAsTable(\"roller_coaster\");\n", "labels": {"reads": [{"table": "mobile_plans", "columns": null}], "writes": [{"table": "roller_coaster", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"platform_production\");\ndf.write().mode(\"overwrite\").saveAsTable(\"appliances\");\n", "labels": {"reads": [{"table": "platform_production", "columns": null}], "writes": [{"table": "appliances", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO criminalcases SELECT population, offer_id, aircraft, playdate FROM cybersecurity_vulnerabilities WHERE population > 340\");\n", "labels": {"reads": [{"table": "cybersecurity_vulnerabilities", "columns": ["population", "offer_id", "aircraft", "playdate"]}], "writes": [{"table": "criminalcases", "columns": ["population", "offer_id", "aircraft", "playdate"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO reverselogisticstransactions SELECT business_size, strat_name, video_id FROM cargo_equipment WHERE business_size > 222\");\n", "labels": {"reads": [{"table": "cargo_equipment", "columns": ["business_size", "strat_name", "video_id"]}], "writes": [{"table": "reverselogisticstransactions", "columns": ["business_size", "strat_name", "video_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO investment_rounds SELECT a.biome_id, b.test_result FROM artsheritage a JOIN stg.stg_inventory_hourly b ON a.dish = b.dish\"\n", "labels": {"reads": [{"table": "artsheritage", "columns": null}, {"table": "stg.stg_inventory_hourly", "columns": null}], "writes": [{"table": "investment_rounds", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 197;\nSQL\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": ["date_of_transaction", "zipcode"]}, {"table": "billstatus", "columns": ["wage", "hometeam"]}], "writes": [{"table": "product_review", "columns": ["wage", "hometeam"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO coal_reserves SELECT injury_count, sale_id, railway_id FROM tourism WHERE injury_count > 483\"], check=True)\n", "labels": {"reads": [{"table": "tourism", "columns": ["injury_count", "sale_id", "railway_id"]}], "writes": [{"table": "coal_reserves", "columns": ["injury_count", "sale_id", "railway_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"artifactanalysis\")\nsrc.write.insertInto(\"ngo_funding\", overwrite=True)\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": null}], "writes": [{"table": "ngo_funding", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO retail_workers_union SELECT safety_record, investment_id, num_developments, goals FROM factories WHERE safety_record > 98\")\n", "labels": {"reads": [{"table": "factories", "columns": ["safety_record", "investment_id", "num_developments", "goals"]}], "writes": [{"table": "retail_workers_union", "columns": ["safety_record", "investment_id", "num_developments", "goals"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO section (transit_passengers, num_beds) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "section", "columns": ["transit_passengers", "num_beds"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"gamedata\")\nupsert_to_store(df, \"station_company\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gamedata", "columns": null}], "writes": [{"table": "station_company", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart_cart_item_di SELECT water_temp, requestdate, characteristic_data_type, container_count FROM landfill_capacity_north_america WHERE water_temp > 457\"], check=True)\n", "labels": {"reads": [{"table": "landfill_capacity_north_america", "columns": ["water_temp", "requestdate", "characteristic_data_type", "container_count"]}], "writes": [{"table": "mart_cart_item_di", "columns": ["water_temp", "requestdate", "characteristic_data_type", "container_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"public.crime_types\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "public.crime_types", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"military_expenditure\")\nsrc.write.insertInto(\"ads.ads_exposure_di\", overwrite=True)\n", "labels": {"reads": [{"table": "military_expenditure", "columns": null}], "writes": [{"table": "ads.ads_exposure_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"specieswatertemp\");\ndf.write().mode(\"overwrite\").saveAsTable(\"police_stations\");\n", "labels": {"reads": [{"table": "specieswatertemp", "columns": null}], "writes": [{"table": "police_stations", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table parties --columns publication_year,fare_amount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "parties", "columns": ["publication_year", "fare_amount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sponsorship_donations SELECT a.department_id, b.guest_id FROM forests a JOIN professional_development b ON a.attendanceid = b.attendanceid\"\n", "labels": {"reads": [{"table": "forests", "columns": null}, {"table": "professional_development", "columns": null}], "writes": [{"table": "sponsorship_donations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table healthcareaccess --target-dir /tmp/land\n", "labels": {"reads": [{"table": "healthcareaccess", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO food_assistance SELECT hireyear, dish_type, productname FROM product_characteristics WHERE hireyear > 500\")\n", "labels": {"reads": [{"table": "product_characteristics", "columns": ["hireyear", "dish_type", "productname"]}], "writes": [{"table": "food_assistance", "columns": ["hireyear", "dish_type", "productname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model staff depends on asteroids\ndbt run --models staff --vars 'source: asteroids'\n", "labels": {"reads": [{"table": "asteroids", "columns": null}], "writes": [{"table": "staff", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO marine_life_populations SELECT a.call_date, b.operationdate FROM continent a JOIN student b ON a.is_sustainable = b.is_sustainable\"\n", "labels": {"reads": [{"table": "continent", "columns": null}, {"table": "student", "columns": null}], "writes": [{"table": "marine_life_populations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"climate_mitigation_projects\")\nupsert_to_output(df, \"donationprograms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "climate_mitigation_projects", "columns": null}], "writes": [{"table": "donationprograms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table member_of --target-dir /tmp/land\n", "labels": {"reads": [{"table": "member_of", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemical_production_5\").toPandas()\ndf[[\"sustainability_score\", \"credit_score\"]].to_sql(\"entrepreneur\", engine, index=False)\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": null}], "writes": [{"table": "entrepreneur", "columns": ["sustainability_score", "credit_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donation\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "donation", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO carbon_offsets SELECT points_per_game, price FROM highways WHERE points_per_game > 167\")\n", "labels": {"reads": [{"table": "highways", "columns": ["points_per_game", "price"]}], "writes": [{"table": "carbon_offsets", "columns": ["points_per_game", "price"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"athletes\")\nsrc.write.insertInto(\"visitor_exhibition\", overwrite=True)\n", "labels": {"reads": [{"table": "athletes", "columns": null}], "writes": [{"table": "visitor_exhibition", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT effective_date, spacecraft_id FROM ods.sessions\", engine)\nif not rows:\n logger.warning('empty result')\nimport logging\ndf.to_sql(\"climateresearch\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.sessions", "columns": ["effective_date", "spacecraft_id"]}], "writes": [{"table": "climateresearch", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO open_pedagogy_courses SELECT model_year, campaign, no_of_loans FROM pets WHERE model_year > 357\")\n", "labels": {"reads": [{"table": "pets", "columns": ["model_year", "campaign", "no_of_loans"]}], "writes": [{"table": "open_pedagogy_courses", "columns": ["model_year", "campaign", "no_of_loans"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.coupon_use_di\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws.coupon_use_di", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessel_tracking\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "vessel_tracking", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"operate_company\")\nsrc.write.insertInto(\"crime_reports\", overwrite=True)\n", "labels": {"reads": [{"table": "operate_company", "columns": null}], "writes": [{"table": "crime_reports", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"bus_routes\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "bus_routes", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO restaurants_tx SELECT clinic_id, product_color FROM ads_users_hourly WHERE clinic_id > 115\"\n", "labels": {"reads": [{"table": "ads_users_hourly", "columns": ["clinic_id", "product_color"]}], "writes": [{"table": "restaurants_tx", "columns": ["clinic_id", "product_color"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO tb_reports SELECT 1\"\nlogger.info(msg)\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model mart_campaigns_daily depends on energy_prices\ndbt build --select mart_campaigns_daily --vars '{\"source_table\":\"energy_prices\"}'\n", "labels": {"reads": [{"table": "energy_prices", "columns": null}], "writes": [{"table": "mart_campaigns_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 92;\nSQL\n", "labels": {"reads": [{"table": "euro_champs_track_field", "columns": ["application", "membership_amount"]}, {"table": "distributors", "columns": ["ship_name", "exhibit_location", "coalquantity"]}], "writes": [{"table": "public.police_calls", "columns": ["ship_name", "exhibit_location", "coalquantity"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO regulatoryframeworksbycountry SELECT tour_id, product_size FROM labor_statistics WHERE tour_id > 252\")\n", "labels": {"reads": [{"table": "labor_statistics", "columns": ["tour_id", "product_size"]}], "writes": [{"table": "regulatoryframeworksbycountry", "columns": ["tour_id", "product_size"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ods_cart_item_df\"\n", "labels": {"reads": [{"table": "ods_cart_item_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"broadband_revenue\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "broadband_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"org_donation\").where(\"dt = current_date()\").writeTo(\"chemicals\").append()\n", "labels": {"reads": [{"table": "org_donation", "columns": null}], "writes": [{"table": "chemicals", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"levees\");\ndf.write().mode(\"overwrite\").saveAsTable(\"reporters\");\n", "labels": {"reads": [{"table": "levees", "columns": null}], "writes": [{"table": "reporters", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nsql = \"INSERT INTO trade_history SELECT a.num_workers, b.src_apid FROM bi.bi_vendors_di a JOIN cars b ON a.teacher_id = b.teacher_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi.bi_vendors_di", "columns": null}, {"table": "cars", "columns": null}], "writes": [{"table": "trade_history", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO dwd.dwd_device_log_delta SELECT a.target_u_id, b.year_founded FROM visitor_exhibition a JOIN vesselfuel b ON a.inspection_id = b.inspection_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "visitor_exhibition", "columns": null}, {"table": "vesselfuel", "columns": null}], "writes": [{"table": "dwd.dwd_device_log_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO vessel_incident_count SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO customers_policies SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"festivals\").where(\"dt = current_date()\").writeTo(\"mealtypes\").append()\n", "labels": {"reads": [{"table": "festivals", "columns": null}], "writes": [{"table": "mealtypes", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO cycling SELECT shipping_agent_name, claimtype FROM dws_events_df WHERE shipping_agent_name > 245\");\n", "labels": {"reads": [{"table": "dws_events_df", "columns": ["shipping_agent_name", "claimtype"]}], "writes": [{"table": "cycling", "columns": ["shipping_agent_name", "claimtype"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO usdaviolations SELECT * FROM legacy\nspark.sql(\"INSERT INTO contractorsales SELECT commanding_officer, founding_location, emp_jobcode FROM nba WHERE commanding_officer > 345\")\n", "labels": {"reads": [{"table": "nba", "columns": ["commanding_officer", "founding_location", "emp_jobcode"]}], "writes": [{"table": "contractorsales", "columns": ["commanding_officer", "founding_location", "emp_jobcode"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO match_result SELECT launch_company, device_id, document_structure_code, technician_id FROM communitycenters WHERE launch_company > 422\")\n", "labels": {"reads": [{"table": "communitycenters", "columns": ["launch_company", "device_id", "document_structure_code", "technician_id"]}], "writes": [{"table": "match_result", "columns": ["launch_company", "device_id", "document_structure_code", "technician_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 291;\nSQL\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": ["threat_type", "stat_id"]}, {"table": "city_tech", "columns": ["claim_status_name", "black", "causename", "number_thousands"]}], "writes": [{"table": "tencel_sources", "columns": ["claim_status_name", "black", "causename", "number_thousands"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"expenses\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "expenses", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table vessel_positions --columns acc_type,start_time --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "vessel_positions", "columns": ["acc_type", "start_time"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO trains SELECT patient_id, hotel_id, customer_code FROM participation WHERE patient_id > 62\"\n", "labels": {"reads": [{"table": "participation", "columns": ["patient_id", "hotel_id", "customer_code"]}], "writes": [{"table": "trains", "columns": ["patient_id", "hotel_id", "customer_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"docking\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "docking", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 402;\nSQL\n", "labels": {"reads": [{"table": "pets", "columns": ["vehicle_name", "tourist_id"]}, {"table": "ods.sessions", "columns": ["goals", "ship_agent_id", "genre_id"]}], "writes": [{"table": "dwd.dwd_exposure_df", "columns": ["goals", "ship_agent_id", "genre_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ratingdate, registration_id FROM security_incidents LIMIT 437\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "security_incidents", "columns": ["ratingdate", "registration_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT topic, max_speed FROM government_transparency\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"stock_levels\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "government_transparency", "columns": ["topic", "max_speed"]}], "writes": [{"table": "stock_levels", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.founder_country > 5).all()\n# src table: crop_temperature\nengine.execute(\"INSERT INTO ads.inventory_di SELECT * FROM crop_temperature\")\n", "labels": {"reads": [{"table": "crop_temperature", "columns": null}], "writes": [{"table": "ads.inventory_di", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 196;\nSQL\n", "labels": {"reads": [{"table": "patient", "columns": ["dispensary_id", "vr_platform"]}, {"table": "temperature_data", "columns": ["num_owners", "hardware_colours", "donor"]}], "writes": [{"table": "pollution_initiatives", "columns": ["num_owners", "hardware_colours", "donor"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO athletes SELECT hireyear, physician, mar FROM bioprocess.engineering_projects WHERE hireyear > 336\");\n", "labels": {"reads": [{"table": "bioprocess.engineering_projects", "columns": ["hireyear", "physician", "mar"]}], "writes": [{"table": "athletes", "columns": ["hireyear", "physician", "mar"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM laborstatistics\"\n", "labels": {"reads": [{"table": "laborstatistics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO climatedata SELECT rural_area, wifi, low_estimate FROM student_courses WHERE rural_area > 275\")\n", "labels": {"reads": [{"table": "student_courses", "columns": ["rural_area", "wifi", "low_estimate"]}], "writes": [{"table": "climatedata", "columns": ["rural_area", "wifi", "low_estimate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO checking SELECT membername, last_service, no_of_loans, disease FROM management WHERE membername > 230\"\n", "labels": {"reads": [{"table": "management", "columns": ["membername", "last_service", "no_of_loans", "disease"]}], "writes": [{"table": "checking", "columns": ["membername", "last_service", "no_of_loans", "disease"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO vehicle_safety_testing SELECT stars, certification, maintenanceid FROM ads.events WHERE stars > 46\");\n", "labels": {"reads": [{"table": "ads.events", "columns": ["stars", "certification", "maintenanceid"]}], "writes": [{"table": "vehicle_safety_testing", "columns": ["stars", "certification", "maintenanceid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO paintings SELECT feature_details, paperid, ethnicity FROM ads.risk_score WHERE feature_details > 241\"\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": ["feature_details", "paperid", "ethnicity"]}], "writes": [{"table": "paintings", "columns": ["feature_details", "paperid", "ethnicity"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 129;\nSQL\n", "labels": {"reads": [{"table": "ads.inventory_di", "columns": ["dec", "transportation_method"]}, {"table": "news_stories", "columns": ["concertid", "platformid", "court_id", "supply_volume"]}], "writes": [{"table": "midwest_region", "columns": ["concertid", "platformid", "court_id", "supply_volume"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO news_stories SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO user_genre SELECT factory_name, governor, hire_date FROM roads WHERE factory_name > 56\")\n", "labels": {"reads": [{"table": "roads", "columns": ["factory_name", "governor", "hire_date"]}], "writes": [{"table": "user_genre", "columns": ["factory_name", "governor", "hire_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table industrial_building_energy_efficiency --target-dir /tmp/land\n", "labels": {"reads": [{"table": "industrial_building_energy_efficiency", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO community_leaders SELECT donor, phone_id FROM districts WHERE donor > 252\")\n", "labels": {"reads": [{"table": "districts", "columns": ["donor", "phone_id"]}], "writes": [{"table": "community_leaders", "columns": ["donor", "phone_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO singer_in_concert SELECT user_login, item_size, away_team_score, subject_name FROM pollution_initiatives WHERE user_login > 289\"\n", "labels": {"reads": [{"table": "pollution_initiatives", "columns": ["user_login", "item_size", "away_team_score", "subject_name"]}], "writes": [{"table": "singer_in_concert", "columns": ["user_login", "item_size", "away_team_score", "subject_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO miningoperations SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"waterconservation\")\nsrc.write.insertInto(\"appointments\", overwrite=True)\n", "labels": {"reads": [{"table": "waterconservation", "columns": null}], "writes": [{"table": "appointments", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"pipelines_us_canada\");\ndf.write().mode(\"overwrite\").saveAsTable(\"course_authors_and_tutors\");\n", "labels": {"reads": [{"table": "pipelines_us_canada", "columns": null}], "writes": [{"table": "course_authors_and_tutors", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO royal_family SELECT principal_activities, studio, first_name, spacecraftid FROM projectemployees WHERE principal_activities > 84\")\n", "labels": {"reads": [{"table": "projectemployees", "columns": ["principal_activities", "studio", "first_name", "spacecraftid"]}], "writes": [{"table": "royal_family", "columns": ["principal_activities", "studio", "first_name", "spacecraftid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table ratings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ratings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO employee SELECT a.interest_group, b.donor_id FROM heritage_sites a JOIN film_market_estimation b ON a.trainingname = b.trainingname\"\n", "labels": {"reads": [{"table": "heritage_sites", "columns": null}, {"table": "film_market_estimation", "columns": null}], "writes": [{"table": "employee", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT staff_address_id, sentence_length FROM energy_prices LIMIT 372\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "energy_prices", "columns": ["staff_address_id", "sentence_length"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"customer_events\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "customer_events", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO trust SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stg.stg_campaigns SELECT dept_store_id, materialid, competition_type, number_of_matches FROM sfc_articles WHERE dept_store_id > 330\"\n", "labels": {"reads": [{"table": "sfc_articles", "columns": ["dept_store_id", "materialid", "competition_type", "number_of_matches"]}], "writes": [{"table": "stg.stg_campaigns", "columns": ["dept_store_id", "materialid", "competition_type", "number_of_matches"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT shipped_to, build_year FROM fabrics\", engine)\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"usdaviolations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "fabrics", "columns": ["shipped_to", "build_year"]}], "writes": [{"table": "usdaviolations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO carbon_prices SELECT violationid, attendeename, content FROM third_party_companies WHERE violationid > 375\"\n", "labels": {"reads": [{"table": "third_party_companies", "columns": ["violationid", "attendeename", "content"]}], "writes": [{"table": "carbon_prices", "columns": ["violationid", "attendeename", "content"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO cinema SELECT * FROM legacy\nspark.sql(\"INSERT INTO supportservices SELECT sale_quantity, dataset FROM first_notification_of_loss WHERE sale_quantity > 493\")\n", "labels": {"reads": [{"table": "first_notification_of_loss", "columns": ["sale_quantity", "dataset"]}], "writes": [{"table": "supportservices", "columns": ["sale_quantity", "dataset"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO dws_cart_item SELECT a.share_count, b.objectnumber FROM higher_ed.students a JOIN buildings b ON a.mediatypeid = b.mediatypeid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "higher_ed.students", "columns": null}, {"table": "buildings", "columns": null}], "writes": [{"table": "dws_cart_item", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.clicks_hourly\", conn)\ndf.to_sql(\"dwd.dwd_users_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.clicks_hourly", "columns": null}], "writes": [{"table": "dwd.dwd_users_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"machinery\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"networkdevices\")\n", "labels": {"reads": [{"table": "machinery", "columns": null}], "writes": [{"table": "networkdevices", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mountain\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mountain", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM customer_contact_channels\", conn)\ndf.to_sql(\"shipments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": null}], "writes": [{"table": "shipments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO co2_emission_reduction SELECT route, principal_activities, indigenous, other_hotel_details FROM tourism_centers WHERE route > 408\"\n", "labels": {"reads": [{"table": "tourism_centers", "columns": ["route", "principal_activities", "indigenous", "other_hotel_details"]}], "writes": [{"table": "co2_emission_reduction", "columns": ["route", "principal_activities", "indigenous", "other_hotel_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO expensive_space_missions SELECT fault_description, workout_type FROM government_transparency WHERE fault_description > 9\"\n", "labels": {"reads": [{"table": "government_transparency", "columns": ["fault_description", "workout_type"]}], "writes": [{"table": "expensive_space_missions", "columns": ["fault_description", "workout_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"textileworkers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"appellations\")\n", "labels": {"reads": [{"table": "textileworkers", "columns": null}], "writes": [{"table": "appellations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.preferred_foot > 250).all()\n# src table: ads.ads_campaigns_full\nengine.execute(\"INSERT INTO humanitarian_operations SELECT * FROM ads.ads_campaigns_full\")\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": null}], "writes": [{"table": "humanitarian_operations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.issue_month > 471).all()\n# src table: fault_log_parts\nengine.execute(\"INSERT INTO defense_personnel SELECT * FROM fault_log_parts\")\n", "labels": {"reads": [{"table": "fault_log_parts", "columns": null}], "writes": [{"table": "defense_personnel", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"department_stores\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"site\")\n", "labels": {"reads": [{"table": "department_stores", "columns": null}], "writes": [{"table": "site", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO france_culture SELECT * FROM legacy\nspark.sql(\"INSERT INTO trenches SELECT name, centerid FROM concert WHERE name > 79\")\n", "labels": {"reads": [{"table": "concert", "columns": ["name", "centerid"]}], "writes": [{"table": "trenches", "columns": ["name", "centerid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT response_time, date_formed FROM ticketspending\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"drug_sales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ticketspending", "columns": ["response_time", "date_formed"]}], "writes": [{"table": "drug_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"residents_services\");\ndf.write().mode(\"overwrite\").saveAsTable(\"suppliersfairlabor\");\n", "labels": {"reads": [{"table": "residents_services", "columns": null}], "writes": [{"table": "suppliersfairlabor", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ai_ethics (festival_name, artistid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ai_ethics", "columns": ["festival_name", "artistid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ytterbiumproduction\", conn)\ndf.to_sql(\"co2_emission_reduction\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ytterbiumproduction", "columns": null}], "writes": [{"table": "co2_emission_reduction", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"marketing_regions\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "marketing_regions", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM marine_species_arctic_ocean\", conn)\ndf.to_sql(\"imagery_archive\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "marine_species_arctic_ocean", "columns": null}], "writes": [{"table": "imagery_archive", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"recycling_rates_oceania\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"students_lifelong_learning\")\n", "labels": {"reads": [{"table": "recycling_rates_oceania", "columns": null}], "writes": [{"table": "students_lifelong_learning", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads_cart_item_hourly\"\n", "labels": {"reads": [{"table": "ads_cart_item_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 387;\nSQL\n", "labels": {"reads": [{"table": "music_events", "columns": ["stu_num", "studio"]}, {"table": "suppliersfairlabor", "columns": ["bill_id", "home_team_points"]}], "writes": [{"table": "programoutcomes", "columns": ["bill_id", "home_team_points"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"storage\")\nsrc.write.insertInto(\"port\", overwrite=True)\n", "labels": {"reads": [{"table": "storage", "columns": null}], "writes": [{"table": "port", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO org_comms SELECT statement_details, facid, headquarters, project_id FROM product_info WHERE statement_details > 74\"\n", "labels": {"reads": [{"table": "product_info", "columns": ["statement_details", "facid", "headquarters", "project_id"]}], "writes": [{"table": "org_comms", "columns": ["statement_details", "facid", "headquarters", "project_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO container_ships SELECT languageid, document_type_description, album FROM animal_budget WHERE languageid > 314\"\n", "labels": {"reads": [{"table": "animal_budget", "columns": ["languageid", "document_type_description", "album"]}], "writes": [{"table": "container_ships", "columns": ["languageid", "document_type_description", "album"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO therapy_attendance SELECT site_name, supplier_country FROM county_public_safety WHERE site_name > 404\"\n", "labels": {"reads": [{"table": "county_public_safety", "columns": ["site_name", "supplier_country"]}], "writes": [{"table": "therapy_attendance", "columns": ["site_name", "supplier_country"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM healthbudget\"\n", "labels": {"reads": [{"table": "healthbudget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO maintenance_engineers SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM nba\"\n", "labels": {"reads": [{"table": "nba", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO disaster_mitigation SELECT transaction_type_code, popularity FROM researchgrants WHERE transaction_type_code > 148\")\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["transaction_type_code", "popularity"]}], "writes": [{"table": "disaster_mitigation", "columns": ["transaction_type_code", "popularity"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO workforce SELECT condition, manufacturer_name FROM grapes WHERE condition > 334\")\n", "labels": {"reads": [{"table": "grapes", "columns": ["condition", "manufacturer_name"]}], "writes": [{"table": "workforce", "columns": ["condition", "manufacturer_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO solar_plants SELECT a.cows, b.goal_id FROM wastewater_facilities a JOIN public.developers b ON a.mission_id = b.mission_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "wastewater_facilities", "columns": null}, {"table": "public.developers", "columns": null}], "writes": [{"table": "solar_plants", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO healthequitymetrics SELECT release_date, co2_offset_amount, acc_regular_season FROM trust WHERE release_date > 403\"\n", "labels": {"reads": [{"table": "trust", "columns": ["release_date", "co2_offset_amount", "acc_regular_season"]}], "writes": [{"table": "healthequitymetrics", "columns": ["release_date", "co2_offset_amount", "acc_regular_season"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"catalogs\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "catalogs", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO dw_member_point_full SELECT orgname, home_team_points, nationality FROM uel_top10 WHERE orgname > 459\");\n", "labels": {"reads": [{"table": "uel_top10", "columns": ["orgname", "home_team_points", "nationality"]}], "writes": [{"table": "dw_member_point_full", "columns": ["orgname", "home_team_points", "nationality"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT subscriber_id, implementation_date FROM arctic_marine_species LIMIT 159\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "arctic_marine_species", "columns": ["subscriber_id", "implementation_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart_refunds\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ods_payments_delta\")\n", "labels": {"reads": [{"table": "mart_refunds", "columns": null}], "writes": [{"table": "ods_payments_delta", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table participants --columns grant_start_date,team_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "participants", "columns": ["grant_start_date", "team_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ai_ethics_policies\", conn)\ndf.to_sql(\"cybersecurity_strategies\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": null}], "writes": [{"table": "cybersecurity_strategies", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ods.shipments_df\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table economic_diversification_efforts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"restaurant\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "restaurant", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"wellbeing_programs\").where(\"dt = current_date()\").writeTo(\"caribbeansea\").append()\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": null}], "writes": [{"table": "caribbeansea", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO address SELECT card_number, amount_donated, providerid, jobcategory FROM smart_contracts_table WHERE card_number > 419\"\n", "labels": {"reads": [{"table": "smart_contracts_table", "columns": ["card_number", "amount_donated", "providerid", "jobcategory"]}], "writes": [{"table": "address", "columns": ["card_number", "amount_donated", "providerid", "jobcategory"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"policy_advocacy\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws.events\");\n", "labels": {"reads": [{"table": "policy_advocacy", "columns": null}], "writes": [{"table": "dws.events", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO safety_incidents_india SELECT payment_id, enroll_grade, volunteer_hours FROM zipcodes WHERE payment_id > 66\"\n", "labels": {"reads": [{"table": "zipcodes", "columns": ["payment_id", "enroll_grade", "volunteer_hours"]}], "writes": [{"table": "safety_incidents_india", "columns": ["payment_id", "enroll_grade", "volunteer_hours"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT stream_id, project_category FROM ads_sessions_di LIMIT 232\")\nrows = cur.fetchall()\nimport logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ads_sessions_di", "columns": ["stream_id", "project_category"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"union_members\")\nsave_to_target(df, \"dws.dws_inventory_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "union_members", "columns": null}], "writes": [{"table": "dws.dws_inventory_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.coupon_use SELECT a.monthly_rental, b.cityname FROM student_course_registrations a JOIN tech_accessibility_funding b ON a.blockcode = b.blockcode\"\n", "labels": {"reads": [{"table": "student_course_registrations", "columns": null}, {"table": "tech_accessibility_funding", "columns": null}], "writes": [{"table": "stg.coupon_use", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO savings_programs SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_table(ctx, \"eventlocations\")\npush_to_output(df, \"activities\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "eventlocations", "columns": null}], "writes": [{"table": "activities", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"climate_investments\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "climate_investments", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT strain, call_time FROM player_coach LIMIT 166\")\nimport logging\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO financialwellbeing SELECT socialimpactscore, county_name, community_name FROM environmentalimpact WHERE socialimpactscore > 420\")\n", "labels": {"reads": [{"table": "player_coach", "columns": ["strain", "call_time"]}, {"table": "environmentalimpact", "columns": ["socialimpactscore", "county_name", "community_name"]}], "writes": [{"table": "financialwellbeing", "columns": ["socialimpactscore", "county_name", "community_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO topublictransportation SELECT production_cost, origin, acc_type, teamname FROM cargo_equipment WHERE production_cost > 45\"\n", "labels": {"reads": [{"table": "cargo_equipment", "columns": ["production_cost", "origin", "acc_type", "teamname"]}], "writes": [{"table": "topublictransportation", "columns": ["production_cost", "origin", "acc_type", "teamname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"investments\");\ndf.write().mode(\"overwrite\").saveAsTable(\"awards\");\n", "labels": {"reads": [{"table": "investments", "columns": null}], "writes": [{"table": "awards", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"economic_diversification_efforts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"film_actor\")\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": null}], "writes": [{"table": "film_actor", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_master_index\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "customer_master_index", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO legal_precedents SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT starttime, store_phone FROM mart.shipments_delta LIMIT 190\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO garmentproduction SELECT investment, supporter, heritage_site_id, blockcode FROM genre_songs WHERE investment > 258\")\n", "labels": {"reads": [{"table": "mart.shipments_delta", "columns": ["starttime", "store_phone"]}, {"table": "genre_songs", "columns": ["investment", "supporter", "heritage_site_id", "blockcode"]}], "writes": [{"table": "garmentproduction", "columns": ["investment", "supporter", "heritage_site_id", "blockcode"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dwd.vendors SELECT a.sales_id, b.track_id FROM hair_care_sales a JOIN clinics b ON a.organic_matter = b.organic_matter\"\n", "labels": {"reads": [{"table": "hair_care_sales", "columns": null}, {"table": "clinics", "columns": null}], "writes": [{"table": "dwd.vendors", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"ytterbium_supply\")\nsrc.write.insertInto(\"bi.bi_events_full\", overwrite=True)\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": null}], "writes": [{"table": "bi.bi_events_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO demographics (service_id, authid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "demographics", "columns": ["service_id", "authid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"workplace_safety\");\ndf.write().mode(\"overwrite\").saveAsTable(\"journal_committee\");\n", "labels": {"reads": [{"table": "workplace_safety", "columns": null}], "writes": [{"table": "journal_committee", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO hospitallocations SELECT * FROM legacy\ncur.execute(\"SELECT donortype, album FROM total_consumption LIMIT 114\")\n", "labels": {"reads": [{"table": "total_consumption", "columns": ["donortype", "album"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO movie_financials SELECT * FROM legacy\nspark.sql(\"INSERT INTO dw.dw_sessions_full SELECT amount_due, staystart, wellname FROM climate_adaptation_re WHERE amount_due > 142\")\n", "labels": {"reads": [{"table": "climate_adaptation_re", "columns": ["amount_due", "staystart", "wellname"]}], "writes": [{"table": "dw.dw_sessions_full", "columns": ["amount_due", "staystart", "wellname"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"blockchain_tech\")\nsrc.write.insertInto(\"shared_escooters\", overwrite=True)\n", "labels": {"reads": [{"table": "blockchain_tech", "columns": null}], "writes": [{"table": "shared_escooters", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ads_payments_hourly\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"bi.bi_payments_full\")\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": null}], "writes": [{"table": "bi.bi_payments_full", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.bi_campaigns_daily SELECT garment_name, city_id, created_date FROM workout_data WHERE garment_name > 320\"\n", "labels": {"reads": [{"table": "workout_data", "columns": ["garment_name", "city_id", "created_date"]}], "writes": [{"table": "bi.bi_campaigns_daily", "columns": ["garment_name", "city_id", "created_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"department_publications\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "department_publications", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO dispensary_sales SELECT a.initiativeid, b.date_test_taken FROM postseason a JOIN coal b ON a.project_name = b.project_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "postseason", "columns": null}, {"table": "coal", "columns": null}], "writes": [{"table": "dispensary_sales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO eco_diversification_investment SELECT deliverydate, restaurantid FROM ods.sessions_daily WHERE deliverydate > 389\")\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": ["deliverydate", "restaurantid"]}], "writes": [{"table": "eco_diversification_investment", "columns": ["deliverydate", "restaurantid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO prices SELECT metric_id, volunteer_id FROM sustainable_sourcing WHERE metric_id > 444\"\n", "labels": {"reads": [{"table": "sustainable_sourcing", "columns": ["metric_id", "volunteer_id"]}], "writes": [{"table": "prices", "columns": ["metric_id", "volunteer_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table tasks --columns volunteerhourid,tournament_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "tasks", "columns": ["volunteerhourid", "tournament_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO volume SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_payment_made, meter_100 FROM all_documents LIMIT 52\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "all_documents", "columns": ["date_payment_made", "meter_100"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mobile_customers_global\"\n", "labels": {"reads": [{"table": "mobile_customers_global", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO dws.dws_events_df SELECT strat_name, museum_id FROM publications WHERE strat_name > 82\")\n", "labels": {"reads": [{"table": "publications", "columns": ["strat_name", "museum_id"]}], "writes": [{"table": "dws.dws_events_df", "columns": ["strat_name", "museum_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"fieldd_info\")\nwrite_to_output(df, \"threat_intelligence_budget\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fieldd_info", "columns": null}], "writes": [{"table": "threat_intelligence_budget", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM material\", conn)\ndf.to_sql(\"discount_coupons\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "material", "columns": null}], "writes": [{"table": "discount_coupons", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart_refunds SELECT company, arrival_time FROM research.species WHERE company > 337\"], check=True)\n", "labels": {"reads": [{"table": "research.species", "columns": ["company", "arrival_time"]}], "writes": [{"table": "mart_refunds", "columns": ["company", "arrival_time"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"eventdates\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "eventdates", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO cultural_heritage SELECT country_name, registered_date, ei_category, area_ha FROM tb_reports WHERE country_name > 469\"\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["country_name", "registered_date", "ei_category", "area_ha"]}], "writes": [{"table": "cultural_heritage", "columns": ["country_name", "registered_date", "ei_category", "area_ha"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO legal_technology_funding (impact, satellite_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "legal_technology_funding", "columns": ["impact", "satellite_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM communities\"\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO people (mgr_start_date, client_first_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "people", "columns": ["mgr_start_date", "client_first_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO rural_clinics (dish, material) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "rural_clinics", "columns": ["dish", "material"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model traveler depends on spacecraft_manufacturing\ndbt build -s traveler --vars '{\"source_table\":\"spacecraft_manufacturing\"}'\n", "labels": {"reads": [{"table": "spacecraft_manufacturing", "columns": null}], "writes": [{"table": "traveler", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"landfill_capacity\")\nsrc.write.insertInto(\"labor_unions\", overwrite=True)\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": null}], "writes": [{"table": "labor_unions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO community_policing (activity_id, compatible_since_year) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "community_policing", "columns": ["activity_id", "compatible_since_year"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table albums --target-dir /tmp/land\n", "labels": {"reads": [{"table": "albums", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"branch\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"salinity_readings\")\n", "labels": {"reads": [{"table": "branch", "columns": null}], "writes": [{"table": "salinity_readings", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 471;\nSQL\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": ["snatch", "credits"]}, {"table": "chemicalbatches", "columns": ["quarter", "retweets"]}], "writes": [{"table": "algorithmic_fairness_incidents", "columns": ["quarter", "retweets"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO gradeconversion SELECT a.salesperson, b.machinery_id FROM public.police_calls a JOIN mappinglengths b ON a.transaction_category = b.transaction_category\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "public.police_calls", "columns": null}, {"table": "mappinglengths", "columns": null}], "writes": [{"table": "gradeconversion", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"skills\").toPandas()\ndf[[\"mediatorid\", \"fair_trade\"]].to_sql(\"premises\", engine, index=False)\n", "labels": {"reads": [{"table": "skills", "columns": null}], "writes": [{"table": "premises", "columns": ["mediatorid", "fair_trade"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO explainableai (problem_description, fault_status) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "explainableai", "columns": ["problem_description", "fault_status"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO lives_in SELECT fda_approved, evaluationid, policy_type_code FROM cultural_events WHERE fda_approved > 296\"\n", "labels": {"reads": [{"table": "cultural_events", "columns": ["fda_approved", "evaluationid", "policy_type_code"]}], "writes": [{"table": "lives_in", "columns": ["fda_approved", "evaluationid", "policy_type_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT completed_course, sellingprice FROM show LIMIT 231\")\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO audience_demographics SELECT student_details, publish_date, max_gust_speed_mph, genrename FROM scan_dates WHERE student_details > 463\")\n", "labels": {"reads": [{"table": "show", "columns": ["completed_course", "sellingprice"]}, {"table": "scan_dates", "columns": ["student_details", "publish_date", "max_gust_speed_mph", "genrename"]}], "writes": [{"table": "audience_demographics", "columns": ["student_details", "publish_date", "max_gust_speed_mph", "genrename"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model contracts depends on accommodations\ndbt run --models contracts --vars '{\"src\":\"accommodations\"}'\n", "labels": {"reads": [{"table": "accommodations", "columns": null}], "writes": [{"table": "contracts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO housingaffordability SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO mart.clicks_delta SELECT campaign, avg_usage, workoutdate FROM species_forests WHERE campaign > 65\"\n", "labels": {"reads": [{"table": "species_forests", "columns": ["campaign", "avg_usage", "workoutdate"]}], "writes": [{"table": "mart.clicks_delta", "columns": ["campaign", "avg_usage", "workoutdate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO lead_mines SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO wastewatertreatment SELECT visit_month, programname FROM bi.refunds_daily WHERE visit_month > 246\"\n", "labels": {"reads": [{"table": "bi.refunds_daily", "columns": ["visit_month", "programname"]}], "writes": [{"table": "wastewatertreatment", "columns": ["visit_month", "programname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg_orders_hourly (detection_date, assessment_score) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg_orders_hourly", "columns": ["detection_date", "assessment_score"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO collective_bargaining SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM safety_incidents_india\"\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_events_daily\").toPandas()\ndf[[\"passenger_id\", \"delegate\"]].to_sql(\"rural_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "ods.ods_events_daily", "columns": null}], "writes": [{"table": "rural_projects", "columns": ["passenger_id", "delegate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO playerscores SELECT shippeddate, fabrictype, media_outlet FROM chemical_production_3 WHERE shippeddate > 301\"\n", "labels": {"reads": [{"table": "chemical_production_3", "columns": ["shippeddate", "fabrictype", "media_outlet"]}], "writes": [{"table": "playerscores", "columns": ["shippeddate", "fabrictype", "media_outlet"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO investors SELECT a.eco_certified, b.fleet_id FROM ads.ads_refunds_hourly a JOIN production_costs b ON a.releasedate = b.releasedate\"\n", "labels": {"reads": [{"table": "ads.ads_refunds_hourly", "columns": null}, {"table": "production_costs", "columns": null}], "writes": [{"table": "investors", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT institution_id, is_accessible FROM donationsbycause LIMIT 311\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "donationsbycause", "columns": ["institution_id", "is_accessible"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stg.stg_events_di\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dailyapplestreams\");\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": null}], "writes": [{"table": "dailyapplestreams", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"donations\")\nsrc.write.insertInto(\"dwd.users_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "donations", "columns": null}], "writes": [{"table": "dwd.users_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM phishing_targets\"\n", "labels": {"reads": [{"table": "phishing_targets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sales_region\").toPandas()\ndf[[\"characteristic_type_code\", \"event_attendance\"]].to_sql(\"tokyo_water_consumption\", engine, index=False)\n", "labels": {"reads": [{"table": "sales_region", "columns": null}], "writes": [{"table": "tokyo_water_consumption", "columns": ["characteristic_type_code", "event_attendance"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dorm\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dws.exposure_df\")\n", "labels": {"reads": [{"table": "dorm", "columns": null}], "writes": [{"table": "dws.exposure_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO ocean_pollution SELECT date_in_locaton_to, label_id FROM fans WHERE date_in_locaton_to > 455\"\n", "labels": {"reads": [{"table": "fans", "columns": ["date_in_locaton_to", "label_id"]}], "writes": [{"table": "ocean_pollution", "columns": ["date_in_locaton_to", "label_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.yield_per_acre > 296).all()\n# src table: monitoring_zones\nengine.execute(\"INSERT INTO marine_species_observations SELECT * FROM monitoring_zones\")\n", "labels": {"reads": [{"table": "monitoring_zones", "columns": null}], "writes": [{"table": "marine_species_observations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT parent_organization_id, response_time FROM site LIMIT 429\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO water_sources SELECT form_type_code, hardware_model_name FROM creative_ai_applications WHERE form_type_code > 22\")\n", "labels": {"reads": [{"table": "site", "columns": ["parent_organization_id", "response_time"]}, {"table": "creative_ai_applications", "columns": ["form_type_code", "hardware_model_name"]}], "writes": [{"table": "water_sources", "columns": ["form_type_code", "hardware_model_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 76;\nEOF\n", "labels": {"reads": [{"table": "cultural_competency_program", "columns": ["facilityid", "cust_id", "artifact_id"]}], "writes": [{"table": "ads.ads_orders", "columns": ["facilityid", "cust_id", "artifact_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO dw.dw_users_di SELECT license_type, played, astronaut_name FROM contributions WHERE license_type > 451\"\n", "labels": {"reads": [{"table": "contributions", "columns": ["license_type", "played", "astronaut_name"]}], "writes": [{"table": "dw.dw_users_di", "columns": ["license_type", "played", "astronaut_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO inspection SELECT supplychainid, daily_co2_emission, rest_id, role_description FROM renewableenergy WHERE supplychainid > 389\");\n", "labels": {"reads": [{"table": "renewableenergy", "columns": ["supplychainid", "daily_co2_emission", "rest_id", "role_description"]}], "writes": [{"table": "inspection", "columns": ["supplychainid", "daily_co2_emission", "rest_id", "role_description"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT amount, product FROM artsales\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ndf.to_sql(\"drug_approval\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "artsales", "columns": ["amount", "product"]}], "writes": [{"table": "drug_approval", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 14;\nSQL\n", "labels": {"reads": [{"table": "geologicalsurvey", "columns": ["energy_generated", "head_id"]}, {"table": "dailyapplestreams", "columns": ["developer_id", "fare", "creator", "head"]}], "writes": [{"table": "music_events", "columns": ["developer_id", "fare", "creator", "head"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO workforce_development SELECT tourist_id, is_male, bioprocess_name FROM mealtypes WHERE tourist_id > 116\"], check=True)\n", "labels": {"reads": [{"table": "mealtypes", "columns": ["tourist_id", "is_male", "bioprocess_name"]}], "writes": [{"table": "workforce_development", "columns": ["tourist_id", "is_male", "bioprocess_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"measurements\")\nsrc.write.insertInto(\"ods.shipments_df\", overwrite=True)\n", "labels": {"reads": [{"table": "measurements", "columns": null}], "writes": [{"table": "ods.shipments_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT region, showid FROM permit LIMIT 74\")\nmetrics.append(round(score, 4))\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO renewableenergy SELECT address_road, event_type_id, year_join FROM playergamedata WHERE address_road > 222\")\n", "labels": {"reads": [{"table": "permit", "columns": ["region", "showid"]}, {"table": "playergamedata", "columns": ["address_road", "event_type_id", "year_join"]}], "writes": [{"table": "renewableenergy", "columns": ["address_road", "event_type_id", "year_join"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO agricultural_projects SELECT funding_received, crime_rate, treasurer_vote, time_hour FROM caribbeansea WHERE funding_received > 217\")\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["funding_received", "crime_rate", "treasurer_vote", "time_hour"]}], "writes": [{"table": "agricultural_projects", "columns": ["funding_received", "crime_rate", "treasurer_vote", "time_hour"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO attendee_demographics SELECT worker_id, policy_name, movie_id FROM categories WHERE worker_id > 409\"], check=True)\n", "labels": {"reads": [{"table": "categories", "columns": ["worker_id", "policy_name", "movie_id"]}], "writes": [{"table": "attendee_demographics", "columns": ["worker_id", "policy_name", "movie_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessel_positions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stores_2\")\n", "labels": {"reads": [{"table": "vessel_positions", "columns": null}], "writes": [{"table": "stores_2", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO autoshow SELECT advisor, system_id, member_in_charge_id FROM vehicle WHERE advisor > 57\");\n", "labels": {"reads": [{"table": "vehicle", "columns": ["advisor", "system_id", "member_in_charge_id"]}], "writes": [{"table": "autoshow", "columns": ["advisor", "system_id", "member_in_charge_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hospitalname, serviceid FROM climate_communication_projects LIMIT 275\")\nmetrics.append(round(score, 4))\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO person SELECT visit_date, apt_number, mappinglength FROM outcomes WHERE visit_date > 438\")\n", "labels": {"reads": [{"table": "climate_communication_projects", "columns": ["hospitalname", "serviceid"]}, {"table": "outcomes", "columns": ["visit_date", "apt_number", "mappinglength"]}], "writes": [{"table": "person", "columns": ["visit_date", "apt_number", "mappinglength"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 32;\nSQL\n", "labels": {"reads": [{"table": "dw.dw_risk_score_full", "columns": ["updated_at", "number_of_matches"]}, {"table": "musical", "columns": ["athleteid", "account_number", "device_name", "fda_approved"]}], "writes": [{"table": "dwd_risk_score_hourly", "columns": ["athleteid", "account_number", "device_name", "fda_approved"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"product_review\")\nwrite_to_sink(df, \"financialwellbeing\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "product_review", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"dysprosium_mines\")\npersist_to_output(df, \"mart.shipments_full\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dysprosium_mines", "columns": null}], "writes": [{"table": "mart.shipments_full", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model coupon_use_full depends on textile_sourcing\ndbt build --models coupon_use_full --vars 'source: textile_sourcing'\n", "labels": {"reads": [{"table": "textile_sourcing", "columns": null}], "writes": [{"table": "coupon_use_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"strandings\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "strandings", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model textile_suppliers depends on user_interests\ndbt build --select textile_suppliers --vars 'source: user_interests'\n", "labels": {"reads": [{"table": "user_interests", "columns": null}], "writes": [{"table": "textile_suppliers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"measurement\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"visitordemographics\")\n", "labels": {"reads": [{"table": "measurement", "columns": null}], "writes": [{"table": "visitordemographics", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"checking\")\nsrc.write.insertInto(\"yoga\", overwrite=True)\n", "labels": {"reads": [{"table": "checking", "columns": null}], "writes": [{"table": "yoga", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT visit_date, monthlyactiveusers FROM shop LIMIT 307\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "shop", "columns": ["visit_date", "monthlyactiveusers"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.menucategory > 168).all()\n# src table: media_types\nengine.execute(\"INSERT INTO canada_tech SELECT * FROM media_types\")\n", "labels": {"reads": [{"table": "media_types", "columns": null}], "writes": [{"table": "canada_tech", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO drama_workshop_groups (payment_id, prepnurse) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "drama_workshop_groups", "columns": ["payment_id", "prepnurse"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO communitypolicingcenters SELECT provider, base_name FROM bi.products_daily WHERE provider > 407\")\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": ["provider", "base_name"]}], "writes": [{"table": "communitypolicingcenters", "columns": ["provider", "base_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contract_value, concert_id FROM public.police_calls LIMIT 179\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO research_staff SELECT asset_model, calendar FROM dwd_sessions_df WHERE asset_model > 330\")\n", "labels": {"reads": [{"table": "public.police_calls", "columns": ["contract_value", "concert_id"]}, {"table": "dwd_sessions_df", "columns": ["asset_model", "calendar"]}], "writes": [{"table": "research_staff", "columns": ["asset_model", "calendar"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tech_for_social_good SELECT event_type, dob FROM hotel_tech_adoptions WHERE event_type > 380\"\n", "labels": {"reads": [{"table": "hotel_tech_adoptions", "columns": ["event_type", "dob"]}], "writes": [{"table": "tech_for_social_good", "columns": ["event_type", "dob"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO book_club SELECT budget_in_billions, num_sessions, language_id, line_name FROM dws.dws_coupon_use_full WHERE budget_in_billions > 418\");\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_full", "columns": ["budget_in_billions", "num_sessions", "language_id", "line_name"]}], "writes": [{"table": "book_club", "columns": ["budget_in_billions", "num_sessions", "language_id", "line_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT citation_time, year_built FROM stg.events_hourly LIMIT 281\")\nrows = cur.fetchall()\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "stg.events_hourly", "columns": ["citation_time", "year_built"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO arctic_sightings SELECT * FROM legacy\ncur.execute(\"SELECT veteran_unemployment_rate, driller FROM department LIMIT 136\")\n", "labels": {"reads": [{"table": "department", "columns": ["veteran_unemployment_rate", "driller"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO students SELECT commanding_officer, spectators, firstname FROM spacecraft_manufacturing WHERE commanding_officer > 359\");\n", "labels": {"reads": [{"table": "spacecraft_manufacturing", "columns": ["commanding_officer", "spectators", "firstname"]}], "writes": [{"table": "students", "columns": ["commanding_officer", "spectators", "firstname"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO entrepreneur SELECT suppliername, booking_start_date FROM volunteerhours WHERE suppliername > 252\")\n", "labels": {"reads": [{"table": "volunteerhours", "columns": ["suppliername", "booking_start_date"]}], "writes": [{"table": "entrepreneur", "columns": ["suppliername", "booking_start_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ods.shipments_df\", conn)\ndf.to_sql(\"menu\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": null}], "writes": [{"table": "menu", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"courtcases\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart.mart_payments_df\")\n", "labels": {"reads": [{"table": "courtcases", "columns": null}], "writes": [{"table": "mart.mart_payments_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO geneva_motor_show (volunteer_quarter, fund_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "geneva_motor_show", "columns": ["volunteer_quarter", "fund_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_sessions_hourly SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_member_point_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw.dw_member_point_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sustainability_fact SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"employment\")\nexport_to_output(df, \"diversification_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "employment", "columns": null}], "writes": [{"table": "diversification_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"playerscores\");\ndf.write().mode(\"overwrite\").saveAsTable(\"shoes\");\n", "labels": {"reads": [{"table": "playerscores", "columns": null}], "writes": [{"table": "shoes", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.max_wind_speed_mph > 231).all()\n# src table: community_health_centers\nengine.execute(\"INSERT INTO crops SELECT * FROM community_health_centers\")\n", "labels": {"reads": [{"table": "community_health_centers", "columns": null}], "writes": [{"table": "crops", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO view_unit_status SELECT apt_type_code, start FROM therapy_sessions WHERE apt_type_code > 383\")\n", "labels": {"reads": [{"table": "therapy_sessions", "columns": ["apt_type_code", "start"]}], "writes": [{"table": "view_unit_status", "columns": ["apt_type_code", "start"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"permit\")\nsrc.write.insertInto(\"europium_exports\", overwrite=True)\n", "labels": {"reads": [{"table": "permit", "columns": null}], "writes": [{"table": "europium_exports", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"habitat\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dwd.inventory_df\")\n", "labels": {"reads": [{"table": "habitat", "columns": null}], "writes": [{"table": "dwd.inventory_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sent_date, impact FROM ads.ads_cart_item_hourly\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"irrigation_systems\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": ["sent_date", "impact"]}], "writes": [{"table": "irrigation_systems", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 185;\nEOF\n", "labels": {"reads": [{"table": "emergency_categories", "columns": ["train_id", "membership", "host_id", "grant_type"]}], "writes": [{"table": "clothingsales", "columns": ["train_id", "membership", "host_id", "grant_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO union_membership (trial_success_rate, equipment_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "union_membership", "columns": ["trial_success_rate", "equipment_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"atlantic_ocean\");\ndf.write().mode(\"overwrite\").saveAsTable(\"taxi_data\");\n", "labels": {"reads": [{"table": "atlantic_ocean", "columns": null}], "writes": [{"table": "taxi_data", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO circular_economy SELECT unionid, num_owners FROM clinics WHERE unionid > 262\"\n", "labels": {"reads": [{"table": "clinics", "columns": ["unionid", "num_owners"]}], "writes": [{"table": "circular_economy", "columns": ["unionid", "num_owners"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ads.refunds_delta SELECT athlete_id, donorgender FROM dws.dws_refunds_hourly WHERE athlete_id > 38\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_hourly", "columns": ["athlete_id", "donorgender"]}], "writes": [{"table": "ads.refunds_delta", "columns": ["athlete_id", "donorgender"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vaccinations, refugee_name FROM carbon_prices\", engine)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"prescribes\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "carbon_prices", "columns": ["vaccinations", "refugee_name"]}], "writes": [{"table": "prescribes", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO satelliteimagery SELECT num_accessible_tech_centers, bname, billing_city, ai_algorithm_id FROM document_locations WHERE num_accessible_tech_centers > 254\"\n", "labels": {"reads": [{"table": "document_locations", "columns": ["num_accessible_tech_centers", "bname", "billing_city", "ai_algorithm_id"]}], "writes": [{"table": "satelliteimagery", "columns": ["num_accessible_tech_centers", "bname", "billing_city", "ai_algorithm_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO training SELECT * FROM legacy\nspark.sql(\"INSERT INTO bank_info SELECT station_name, chromosome, operation_name, cancel_date FROM benefits_overpayments WHERE station_name > 436\")\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": ["station_name", "chromosome", "operation_name", "cancel_date"]}], "writes": [{"table": "bank_info", "columns": ["station_name", "chromosome", "operation_name", "cancel_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_activity\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stock\")\n", "labels": {"reads": [{"table": "user_activity", "columns": null}], "writes": [{"table": "stock", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO solar_energy SELECT goldid, visit_month, address FROM mart.shipments_full WHERE goldid > 387\");\n", "labels": {"reads": [{"table": "mart.shipments_full", "columns": ["goldid", "visit_month", "address"]}], "writes": [{"table": "solar_energy", "columns": ["goldid", "visit_month", "address"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model education_budget depends on dws.dws_coupon_use_di\ndbt build --select education_budget --vars '{\"src\":\"dws.dws_coupon_use_di\"}'\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_di", "columns": null}], "writes": [{"table": "education_budget", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model smartcontracts depends on ngo_funding\ndbt build -s smartcontracts --vars 'source: ngo_funding'\n", "labels": {"reads": [{"table": "ngo_funding", "columns": null}], "writes": [{"table": "smartcontracts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"beauty_products\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "beauty_products", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model stg_device_log_daily depends on chemical_processes\ndbt run -s stg_device_log_daily --vars 'source: chemical_processes'\n", "labels": {"reads": [{"table": "chemical_processes", "columns": null}], "writes": [{"table": "stg_device_log_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"fish_farms\")\nsrc.write.insertInto(\"communitypolicing\", overwrite=True)\n", "labels": {"reads": [{"table": "fish_farms", "columns": null}], "writes": [{"table": "communitypolicing", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws.dws_refunds_hourly\", conn)\ndf.to_sql(\"ods.ods_member_point_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws.dws_refunds_hourly", "columns": null}], "writes": [{"table": "ods.ods_member_point_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO youth_fan_participation SELECT pediatrician_id, workout_type FROM mobile_plans WHERE pediatrician_id > 78\"\n", "labels": {"reads": [{"table": "mobile_plans", "columns": ["pediatrician_id", "workout_type"]}], "writes": [{"table": "youth_fan_participation", "columns": ["pediatrician_id", "workout_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 154;\nEOF\n", "labels": {"reads": [{"table": "incident_region", "columns": ["views", "pricepergram", "community_center_id"]}], "writes": [{"table": "documents_to_be_destroyed", "columns": ["views", "pricepergram", "community_center_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_table(ctx, \"mineral_extraction_us\")\nupsert_to_store(df, \"agroecology_practices\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mineral_extraction_us", "columns": null}], "writes": [{"table": "agroecology_practices", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mining_companies (resource_id, city_population) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mining_companies", "columns": ["resource_id", "city_population"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.campaigns_daily --columns customer_code,co2_offset_amount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.campaigns_daily", "columns": ["customer_code", "co2_offset_amount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table shipments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "shipments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT num_libraries, dysprosium_prod FROM marine_species LIMIT 438\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO recycling_stats SELECT camera_lens_id, numhearings FROM vehicle_prices WHERE camera_lens_id > 350\")\n", "labels": {"reads": [{"table": "marine_species", "columns": ["num_libraries", "dysprosium_prod"]}, {"table": "vehicle_prices", "columns": ["camera_lens_id", "numhearings"]}], "writes": [{"table": "recycling_stats", "columns": ["camera_lens_id", "numhearings"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT vessel_id, complaintid FROM renewabletypes LIMIT 61\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO fertilizer_usage SELECT player_id, production_value, courtid FROM textile_sourcing WHERE player_id > 100\")\n", "labels": {"reads": [{"table": "renewabletypes", "columns": ["vessel_id", "complaintid"]}, {"table": "textile_sourcing", "columns": ["player_id", "production_value", "courtid"]}], "writes": [{"table": "fertilizer_usage", "columns": ["player_id", "production_value", "courtid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO view_product_availability (decision, monthlyactiveusers) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "view_product_availability", "columns": ["decision", "monthlyactiveusers"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO weather_record SELECT ai_id, impact, method_name FROM drugs WHERE ai_id > 494\"\n", "labels": {"reads": [{"table": "drugs", "columns": ["ai_id", "impact", "method_name"]}], "writes": [{"table": "weather_record", "columns": ["ai_id", "impact", "method_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model locations_oceania depends on ods.campaigns_di\ndbt build --select locations_oceania --vars '{\"src\":\"ods.campaigns_di\"}'\n", "labels": {"reads": [{"table": "ods.campaigns_di", "columns": null}], "writes": [{"table": "locations_oceania", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"whale_sharks\")\npush_to_output(df, \"nomination\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "whale_sharks", "columns": null}], "writes": [{"table": "nomination", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"evsales\").toPandas()\ndf[[\"num_libraries\", \"mine_id\"]].to_sql(\"mart.mart_events_di\", engine, index=False)\n", "labels": {"reads": [{"table": "evsales", "columns": null}], "writes": [{"table": "mart.mart_events_di", "columns": ["num_libraries", "mine_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO inspectiondata SELECT * FROM legacy\ncur.execute(\"SELECT cmi_cross_ref_id, cuisine FROM dwd_coupon_use_hourly LIMIT 79\")\n", "labels": {"reads": [{"table": "dwd_coupon_use_hourly", "columns": ["cmi_cross_ref_id", "cuisine"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT certification, avg_depth FROM organic_products LIMIT 464\")\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO new_schedules SELECT team_id_br, regionid, company_id FROM member_of_club WHERE team_id_br > 56\")\n", "labels": {"reads": [{"table": "organic_products", "columns": ["certification", "avg_depth"]}, {"table": "member_of_club", "columns": ["team_id_br", "regionid", "company_id"]}], "writes": [{"table": "new_schedules", "columns": ["team_id_br", "regionid", "company_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"systems\")\nsrc.write.insertInto(\"incidents\", overwrite=True)\n", "labels": {"reads": [{"table": "systems", "columns": null}], "writes": [{"table": "incidents", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO episodes SELECT conferenceid, port_code, date_left_staff, missiontype FROM show WHERE conferenceid > 450\"], check=True)\n", "labels": {"reads": [{"table": "show", "columns": ["conferenceid", "port_code", "date_left_staff", "missiontype"]}], "writes": [{"table": "episodes", "columns": ["conferenceid", "port_code", "date_left_staff", "missiontype"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO articles SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO volume SELECT * FROM legacy\nspark.sql(\"INSERT INTO dws_coupon_use SELECT don_id, handling_id, chemical_name, maintenance_type FROM movie WHERE don_id > 188\")\n", "labels": {"reads": [{"table": "movie", "columns": ["don_id", "handling_id", "chemical_name", "maintenance_type"]}], "writes": [{"table": "dws_coupon_use", "columns": ["don_id", "handling_id", "chemical_name", "maintenance_type"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 227;\nSQL\n", "labels": {"reads": [{"table": "engineer_skills", "columns": ["skill_description", "materialid"]}, {"table": "vessel_capacity", "columns": ["participation_id", "tourist_attraction_id", "algorithmic_fairness_score", "assessmentname"]}], "writes": [{"table": "algorithmic_fairness", "columns": ["participation_id", "tourist_attraction_id", "algorithmic_fairness_score", "assessmentname"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO birds SELECT app_name, model_id, archeologist FROM winter_olympics WHERE app_name > 73\")\n", "labels": {"reads": [{"table": "winter_olympics", "columns": ["app_name", "model_id", "archeologist"]}], "writes": [{"table": "birds", "columns": ["app_name", "model_id", "archeologist"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"enzyme\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "enzyme", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd.dwd_products\");\ndf.write().mode(\"overwrite\").saveAsTable(\"manufacturingplants\");\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": null}], "writes": [{"table": "manufacturingplants", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 457;\nEOF\n", "labels": {"reads": [{"table": "art_exhibit_attendance", "columns": ["extraction_state", "event_date", "publisher"]}], "writes": [{"table": "attribute_definitions", "columns": ["extraction_state", "event_date", "publisher"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"season_assists\")\nsrc.write.insertInto(\"innovation_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "season_assists", "columns": null}], "writes": [{"table": "innovation_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.refunds_hourly SELECT a.continent, b.warehouse_id FROM worker_scores a JOIN drought_data b ON a.accessibility = b.accessibility\"\n", "labels": {"reads": [{"table": "worker_scores", "columns": null}, {"table": "drought_data", "columns": null}], "writes": [{"table": "stg.refunds_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO africa_schema.african_mines SELECT dispensaryid, subject_id, singer_id, running_time FROM animal_rehab WHERE dispensaryid > 72\");\n", "labels": {"reads": [{"table": "animal_rehab", "columns": ["dispensaryid", "subject_id", "singer_id", "running_time"]}], "writes": [{"table": "africa_schema.african_mines", "columns": ["dispensaryid", "subject_id", "singer_id", "running_time"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO recovery_program SELECT order_id, impressions, primary FROM safety_testing WHERE order_id > 490\")\n", "labels": {"reads": [{"table": "safety_testing", "columns": ["order_id", "impressions", "primary"]}], "writes": [{"table": "recovery_program", "columns": ["order_id", "impressions", "primary"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"indigenouscommunities\");\ndf.write().mode(\"overwrite\").saveAsTable(\"parties\");\n", "labels": {"reads": [{"table": "indigenouscommunities", "columns": null}], "writes": [{"table": "parties", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dw.dw_events_di\");\ndf.write().mode(\"overwrite\").saveAsTable(\"performance_scores\");\n", "labels": {"reads": [{"table": "dw.dw_events_di", "columns": null}], "writes": [{"table": "performance_scores", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"articles_es\")\nsrc.write.insertInto(\"regional_archaeologists\", overwrite=True)\n", "labels": {"reads": [{"table": "articles_es", "columns": null}], "writes": [{"table": "regional_archaeologists", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dysprosium_mines depends on bike_station_info\ndbt build --models dysprosium_mines --vars 'source: bike_station_info'\n", "labels": {"reads": [{"table": "bike_station_info", "columns": null}], "writes": [{"table": "dysprosium_mines", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_shipments_daily\").toPandas()\ndf[[\"gametype\", \"num_solo_exhibitions\"]].to_sql(\"city_waste_generation\", engine, index=False)\n", "labels": {"reads": [{"table": "bi_shipments_daily", "columns": null}], "writes": [{"table": "city_waste_generation", "columns": ["gametype", "num_solo_exhibitions"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO transportation_fleet SELECT * FROM legacy\ncur.execute(\"SELECT stream_id, reason FROM laborstatistics LIMIT 310\")\n", "labels": {"reads": [{"table": "laborstatistics", "columns": ["stream_id", "reason"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 182;\nEOF\n", "labels": {"reads": [{"table": "communityengagement", "columns": ["customer_address", "device", "asset_id", "animal_species"]}], "writes": [{"table": "grapes", "columns": ["customer_address", "device", "asset_id", "animal_species"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"militarycyberops\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "militarycyberops", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO size SELECT * FROM legacy\nspark.sql(\"INSERT INTO continents SELECT pass_fail, location_id, volunteerjoindate FROM projectemployees WHERE pass_fail > 349\")\n", "labels": {"reads": [{"table": "projectemployees", "columns": ["pass_fail", "location_id", "volunteerjoindate"]}], "writes": [{"table": "continents", "columns": ["pass_fail", "location_id", "volunteerjoindate"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"public_transport.passenger_count\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"policyadvocacyevents\")\n", "labels": {"reads": [{"table": "public_transport.passenger_count", "columns": null}], "writes": [{"table": "policyadvocacyevents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.temporary_acting > 144).all()\n# src table: criminalcases\nengine.execute(\"INSERT INTO productsafety SELECT * FROM criminalcases\")\n", "labels": {"reads": [{"table": "criminalcases", "columns": null}], "writes": [{"table": "productsafety", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO manager_award SELECT * FROM legacy\ncur.execute(\"SELECT destination_name, campaign_name FROM building LIMIT 195\")\n", "labels": {"reads": [{"table": "building", "columns": ["destination_name", "campaign_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table cybersecurity_vulnerabilities --target-dir /tmp/land\n", "labels": {"reads": [{"table": "cybersecurity_vulnerabilities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO defense_contractors SELECT show_name, weeks_on_top FROM marine_conservation WHERE show_name > 448\");\n", "labels": {"reads": [{"table": "marine_conservation", "columns": ["show_name", "weeks_on_top"]}], "writes": [{"table": "defense_contractors", "columns": ["show_name", "weeks_on_top"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO customer_events SELECT stu_fname, max_dissolved_oxygen FROM parts WHERE stu_fname > 230\")\n", "labels": {"reads": [{"table": "parts", "columns": ["stu_fname", "max_dissolved_oxygen"]}], "writes": [{"table": "customer_events", "columns": ["stu_fname", "max_dissolved_oxygen"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"gamesales\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"iron_ore_production\")\n", "labels": {"reads": [{"table": "gamesales", "columns": null}], "writes": [{"table": "iron_ore_production", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"oceanography\")\nsrc.write.insertInto(\"restorative_justice_programs\", overwrite=True)\n", "labels": {"reads": [{"table": "oceanography", "columns": null}], "writes": [{"table": "restorative_justice_programs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO program_funding_2 SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"drivers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"sanctuaryanimals\")\n", "labels": {"reads": [{"table": "drivers", "columns": null}], "writes": [{"table": "sanctuaryanimals", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"resource_extraction\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"victims\")\n", "labels": {"reads": [{"table": "resource_extraction", "columns": null}], "writes": [{"table": "victims", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT profits_billion, permit_number FROM immunizationrates\", engine)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"soccer_goals\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "immunizationrates", "columns": ["profits_billion", "permit_number"]}], "writes": [{"table": "soccer_goals", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO all_programs SELECT a.location_description, b.port FROM ecohousing a JOIN city_labor_cost b ON a.moisture_level = b.moisture_level\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ecohousing", "columns": null}, {"table": "city_labor_cost", "columns": null}], "writes": [{"table": "all_programs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO factories SELECT reservoir_id, trial_status, ram_mib FROM whale_sharks WHERE reservoir_id > 61\");\n", "labels": {"reads": [{"table": "whale_sharks", "columns": ["reservoir_id", "trial_status", "ram_mib"]}], "writes": [{"table": "factories", "columns": ["reservoir_id", "trial_status", "ram_mib"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"budget_allocations\").toPandas()\ndf[[\"request\", \"dribbling\"]].to_sql(\"dws.shipments_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "dws.shipments_daily", "columns": ["request", "dribbling"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.wage > 204).all()\n# src table: enrolled_in\nengine.execute(\"INSERT INTO ocean_pollution SELECT * FROM enrolled_in\")\n", "labels": {"reads": [{"table": "enrolled_in", "columns": null}], "writes": [{"table": "ocean_pollution", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM excavation\"\n", "labels": {"reads": [{"table": "excavation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO haircaresales (supplier_country, total) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "haircaresales", "columns": ["supplier_country", "total"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"recyclers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "recyclers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ads.vendors_delta --columns expedition_name,neighborhood_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ads.vendors_delta", "columns": ["expedition_name", "neighborhood_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO street_markets (publication_year, resource) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "street_markets", "columns": ["publication_year", "resource"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 138;\nEOF\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": ["classroom", "impact_id", "theftdate"]}], "writes": [{"table": "intelligence_agents", "columns": ["classroom", "impact_id", "theftdate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sales_quarterly\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"esportsteamsafrica\")\n", "labels": {"reads": [{"table": "sales_quarterly", "columns": null}], "writes": [{"table": "esportsteamsafrica", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM creative_ai_applications\"\n", "labels": {"reads": [{"table": "creative_ai_applications", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 54;\nEOF\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": ["allergy", "market_id"]}], "writes": [{"table": "prereq", "columns": ["allergy", "market_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"candidates\").toPandas()\ndf[[\"attendees\", \"system_id\"]].to_sql(\"oil_production\", engine, index=False)\n", "labels": {"reads": [{"table": "candidates", "columns": null}], "writes": [{"table": "oil_production", "columns": ["attendees", "system_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM team_franchise\"\n", "labels": {"reads": [{"table": "team_franchise", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fieldd_info\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "fieldd_info", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO smart_contracts_table SELECT customer_type_code, catalog_id, case_id, camera_lens_id FROM mine_workforce WHERE customer_type_code > 215\");\n", "labels": {"reads": [{"table": "mine_workforce", "columns": ["customer_type_code", "catalog_id", "case_id", "camera_lens_id"]}], "writes": [{"table": "smart_contracts_table", "columns": ["customer_type_code", "catalog_id", "case_id", "camera_lens_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"canada_tech\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "canada_tech", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model sustainable_building depends on people_addresses\ndbt build -s sustainable_building --vars '{\"source_table\":\"people_addresses\"}'\n", "labels": {"reads": [{"table": "people_addresses", "columns": null}], "writes": [{"table": "sustainable_building", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO ads SELECT ingredient_id, line_1_number_building, date_closed FROM stg.coupon_use WHERE ingredient_id > 332\");\n", "labels": {"reads": [{"table": "stg.coupon_use", "columns": ["ingredient_id", "line_1_number_building", "date_closed"]}], "writes": [{"table": "ads", "columns": ["ingredient_id", "line_1_number_building", "date_closed"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dw.dw_events_di --columns rest_id,fish_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_events_di", "columns": ["rest_id", "fish_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tourism SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO iot_sensors SELECT vaccination_status, spacecraftid, club_name, customer_id FROM procedures WHERE vaccination_status > 15\")\n", "labels": {"reads": [{"table": "procedures", "columns": ["vaccination_status", "spacecraftid", "club_name", "customer_id"]}], "writes": [{"table": "iot_sensors", "columns": ["vaccination_status", "spacecraftid", "club_name", "customer_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM waterconsumptionbyoperation\", conn)\ndf.to_sql(\"therapy_sessions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "waterconsumptionbyoperation", "columns": null}], "writes": [{"table": "therapy_sessions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO platform_production SELECT building_type, is_cruelty_free FROM jp_schema.policy_areas WHERE building_type > 263\"\n", "labels": {"reads": [{"table": "jp_schema.policy_areas", "columns": ["building_type", "is_cruelty_free"]}], "writes": [{"table": "platform_production", "columns": ["building_type", "is_cruelty_free"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"org_climate_finance\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"manufacturing_processes\")\n", "labels": {"reads": [{"table": "org_climate_finance", "columns": null}], "writes": [{"table": "manufacturing_processes", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT form_type_code, launch_date FROM ods.ods_campaigns_delta LIMIT 25\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO part_faults SELECT trip_city, creation_date FROM festivals WHERE trip_city > 364\")\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": ["form_type_code", "launch_date"]}, {"table": "festivals", "columns": ["trip_city", "creation_date"]}], "writes": [{"table": "part_faults", "columns": ["trip_city", "creation_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"list\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"fabricdata\")\n", "labels": {"reads": [{"table": "list", "columns": null}], "writes": [{"table": "fabricdata", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"player\")\nsrc.write.insertInto(\"districts_india\", overwrite=True)\n", "labels": {"reads": [{"table": "player", "columns": null}], "writes": [{"table": "districts_india", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"street_markets\").where(\"dt = current_date()\").writeTo(\"music_streaming\").append()\n", "labels": {"reads": [{"table": "street_markets", "columns": null}], "writes": [{"table": "music_streaming", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"green_buildings\");\ndf.write().mode(\"overwrite\").saveAsTable(\"salesdata\");\n", "labels": {"reads": [{"table": "green_buildings", "columns": null}], "writes": [{"table": "salesdata", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model view_product_availability depends on tour_guides\ndbt run --models view_product_availability --vars 'source: tour_guides'\n", "labels": {"reads": [{"table": "tour_guides", "columns": null}], "writes": [{"table": "view_product_availability", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT extractiondate, mission_category FROM coralreefs LIMIT 414\")\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO whale_sharks SELECT projecttype, has_access, commission_pct, has_disability FROM genetics.crispr WHERE projecttype > 125\")\n", "labels": {"reads": [{"table": "coralreefs", "columns": ["extractiondate", "mission_category"]}, {"table": "genetics.crispr", "columns": ["projecttype", "has_access", "commission_pct", "has_disability"]}], "writes": [{"table": "whale_sharks", "columns": ["projecttype", "has_access", "commission_pct", "has_disability"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM model_fairness\", conn)\ndf.to_sql(\"factory_connections\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "model_fairness", "columns": null}], "writes": [{"table": "factory_connections", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM carbon_prices_3\", conn)\ndf.to_sql(\"ads.orders_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": null}], "writes": [{"table": "ads.orders_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO textile_waste SELECT has_spf, appointment_duration FROM ocean WHERE has_spf > 244\"], check=True)\n", "labels": {"reads": [{"table": "ocean", "columns": ["has_spf", "appointment_duration"]}], "writes": [{"table": "textile_waste", "columns": ["has_spf", "appointment_duration"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO professionals SELECT organization_id, water_consumption FROM locations WHERE organization_id > 489\")\n", "labels": {"reads": [{"table": "locations", "columns": ["organization_id", "water_consumption"]}], "writes": [{"table": "professionals", "columns": ["organization_id", "water_consumption"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cuisine SELECT 1\"\nlogger.info(msg)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO ticketspending SELECT a.shariah_compliant_investment_amount, b.furniture_id FROM state_info a JOIN candidate_assessments b ON a.dish = b.dish\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "state_info", "columns": null}, {"table": "candidate_assessments", "columns": null}], "writes": [{"table": "ticketspending", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO menu SELECT end_date, threat_type FROM caribbean_tourists WHERE end_date > 303\")\n", "labels": {"reads": [{"table": "caribbean_tourists", "columns": ["end_date", "threat_type"]}], "writes": [{"table": "menu", "columns": ["end_date", "threat_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO route_fares SELECT decor, song, milestone, investor_id FROM storage WHERE decor > 383\")\n", "labels": {"reads": [{"table": "storage", "columns": ["decor", "song", "milestone", "investor_id"]}], "writes": [{"table": "route_fares", "columns": ["decor", "song", "milestone", "investor_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table textile_suppliers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "textile_suppliers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws_products\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dws_products", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_tech_adoption\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hotel_tech_adoption", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO transportation_union SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO urban_transportation SELECT size_ha, password, haslegalprecedent, safety_rating FROM highways WHERE size_ha > 426\");\n", "labels": {"reads": [{"table": "highways", "columns": ["size_ha", "password", "haslegalprecedent", "safety_rating"]}], "writes": [{"table": "urban_transportation", "columns": ["size_ha", "password", "haslegalprecedent", "safety_rating"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO displaced_people SELECT organization_name, part_name, swimmer_id, occupation FROM climate_communication_projects WHERE organization_name > 59\"\n", "labels": {"reads": [{"table": "climate_communication_projects", "columns": ["organization_name", "part_name", "swimmer_id", "occupation"]}], "writes": [{"table": "displaced_people", "columns": ["organization_name", "part_name", "swimmer_id", "occupation"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO labor_costs SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ref_attraction_types SELECT steps, funder, incident_type FROM county WHERE steps > 202\")\n", "labels": {"reads": [{"table": "county", "columns": ["steps", "funder", "incident_type"]}], "writes": [{"table": "ref_attraction_types", "columns": ["steps", "funder", "incident_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT target_u_id, dispensary FROM attorney_billing_rates LIMIT 143\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": ["target_u_id", "dispensary"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO environmental_impact_stats SELECT accommodation_type, transaction_date, aircraft_id, date_valid_to FROM marine_species_status WHERE accommodation_type > 214\")\n", "labels": {"reads": [{"table": "marine_species_status", "columns": ["accommodation_type", "transaction_date", "aircraft_id", "date_valid_to"]}], "writes": [{"table": "environmental_impact_stats", "columns": ["accommodation_type", "transaction_date", "aircraft_id", "date_valid_to"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"drought_data\").where(\"dt = current_date()\").writeTo(\"ads.payments_di\").append()\n", "labels": {"reads": [{"table": "drought_data", "columns": null}], "writes": [{"table": "ads.payments_di", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"procedures\")\nsrc.write.insertInto(\"smart_cities\", overwrite=True)\n", "labels": {"reads": [{"table": "procedures", "columns": null}], "writes": [{"table": "smart_cities", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT station_id, booking_status_code FROM dailystreams LIMIT 167\")\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO water_treatment_facilities SELECT time_day, catalog_name, plantlocation FROM train WHERE time_day > 482\")\n", "labels": {"reads": [{"table": "dailystreams", "columns": ["station_id", "booking_status_code"]}, {"table": "train", "columns": ["time_day", "catalog_name", "plantlocation"]}], "writes": [{"table": "water_treatment_facilities", "columns": ["time_day", "catalog_name", "plantlocation"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"performance_scores\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"areas\")\n", "labels": {"reads": [{"table": "performance_scores", "columns": null}], "writes": [{"table": "areas", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"stg.member_point_df\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "stg.member_point_df", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"resource_extraction\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"market_share\")\n", "labels": {"reads": [{"table": "resource_extraction", "columns": null}], "writes": [{"table": "market_share", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO government.region SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table warehouses --columns model,permit_number --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "warehouses", "columns": ["model", "permit_number"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dw.dw_coupon_use_daily --columns trainingtitle,min_salary --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_coupon_use_daily", "columns": ["trainingtitle", "min_salary"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO co2emissions SELECT mental_health_status, cost_id FROM missions WHERE mental_health_status > 230\")\n", "labels": {"reads": [{"table": "missions", "columns": ["mental_health_status", "cost_id"]}], "writes": [{"table": "co2emissions", "columns": ["mental_health_status", "cost_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"ads.ads_payments_hourly\")\nsrc.write.insertInto(\"project_timelines\", overwrite=True)\n", "labels": {"reads": [{"table": "ads.ads_payments_hourly", "columns": null}], "writes": [{"table": "project_timelines", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model endowment depends on country_waste_generation\ndbt build --select endowment --vars '{\"source_table\":\"country_waste_generation\"}'\n", "labels": {"reads": [{"table": "country_waste_generation", "columns": null}], "writes": [{"table": "endowment", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO sports_events SELECT tripid, recycler_id, fabricid FROM green_projects WHERE tripid > 140\"\n", "labels": {"reads": [{"table": "green_projects", "columns": ["tripid", "recycler_id", "fabricid"]}], "writes": [{"table": "sports_events", "columns": ["tripid", "recycler_id", "fabricid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table precipitation_data --columns bus_number,equipment_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "precipitation_data", "columns": ["bus_number", "equipment_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"athlete_stats\")\nsrc.write.insertInto(\"highways\", overwrite=True)\n", "labels": {"reads": [{"table": "athlete_stats", "columns": null}], "writes": [{"table": "highways", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"shoes\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "shoes", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"call_volume\")\nupsert_to_sink(df, \"scores\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "call_volume", "columns": null}], "writes": [{"table": "scores", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO music_streaming SELECT * FROM legacy\nspark.sql(\"INSERT INTO dws.events SELECT clientid, base_name, gamesplayed FROM efforts WHERE clientid > 26\")\n", "labels": {"reads": [{"table": "efforts", "columns": ["clientid", "base_name", "gamesplayed"]}], "writes": [{"table": "dws.events", "columns": ["clientid", "base_name", "gamesplayed"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO spacecraft_temperatures SELECT * FROM legacy\nspark.sql(\"INSERT INTO thefts SELECT purchasedate, statement_id, permitid FROM ads.ads_orders WHERE purchasedate > 72\")\n", "labels": {"reads": [{"table": "ads.ads_orders", "columns": ["purchasedate", "statement_id", "permitid"]}], "writes": [{"table": "thefts", "columns": ["purchasedate", "statement_id", "permitid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 350;\nEOF\n", "labels": {"reads": [{"table": "record", "columns": ["company_name", "ship_name"]}], "writes": [{"table": "org_climate_finance", "columns": ["company_name", "ship_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO urban_farms SELECT retailer_id, unit_of_measure, budget_million FROM agro_regions WHERE retailer_id > 306\"\n", "labels": {"reads": [{"table": "agro_regions", "columns": ["retailer_id", "unit_of_measure", "budget_million"]}], "writes": [{"table": "urban_farms", "columns": ["retailer_id", "unit_of_measure", "budget_million"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO ods_exposure_delta SELECT route_name, amount_paid, project_category, detention_type_code FROM dwd.dwd_campaigns_df WHERE route_name > 358\");\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": ["route_name", "amount_paid", "project_category", "detention_type_code"]}], "writes": [{"table": "ods_exposure_delta", "columns": ["route_name", "amount_paid", "project_category", "detention_type_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model social_impact_bonds depends on therapy\ndbt run --models social_impact_bonds --vars '{\"src\":\"therapy\"}'\n", "labels": {"reads": [{"table": "therapy", "columns": null}], "writes": [{"table": "social_impact_bonds", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO teams SELECT news_outlet, guest_first_name FROM supportservices WHERE news_outlet > 86\"], check=True)\n", "labels": {"reads": [{"table": "supportservices", "columns": ["news_outlet", "guest_first_name"]}], "writes": [{"table": "teams", "columns": ["news_outlet", "guest_first_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"student_mental_health\").where(\"dt = current_date()\").writeTo(\"dwd_coupon_use_hourly\").append()\n", "labels": {"reads": [{"table": "student_mental_health", "columns": null}], "writes": [{"table": "dwd_coupon_use_hourly", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.garmentid > 440).all()\n# src table: bay_area_properties\nengine.execute(\"INSERT INTO ads SELECT * FROM bay_area_properties\")\n", "labels": {"reads": [{"table": "bay_area_properties", "columns": null}], "writes": [{"table": "ads", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table stg.stg_products_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.stg_products_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mining_companies\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mining_companies", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_orders_daily --columns arrival_time,paperid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_orders_daily", "columns": ["arrival_time", "paperid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 263;\nEOF\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["job_category", "address_id", "mean_temperature_f", "year_opened"]}], "writes": [{"table": "plots", "columns": ["job_category", "address_id", "mean_temperature_f", "year_opened"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO bi.bi_member_point SELECT donorage, donortype FROM device WHERE donorage > 160\")\n", "labels": {"reads": [{"table": "device", "columns": ["donorage", "donortype"]}], "writes": [{"table": "bi.bi_member_point", "columns": ["donorage", "donortype"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.bi_risk_score_full SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO campaigns SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vrplayers\", conn)\ndf.to_sql(\"spacex_missions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vrplayers", "columns": null}], "writes": [{"table": "spacex_missions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT species_name, budget_amount FROM investor_activities LIMIT 147\")\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO community_health_workers SELECT digital_channel, dysprosium_prod, crop FROM foodaid WHERE digital_channel > 192\")\n", "labels": {"reads": [{"table": "investor_activities", "columns": ["species_name", "budget_amount"]}, {"table": "foodaid", "columns": ["digital_channel", "dysprosium_prod", "crop"]}], "writes": [{"table": "community_health_workers", "columns": ["digital_channel", "dysprosium_prod", "crop"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.clicks SELECT comment_count, financial_wellbeing_score FROM higher_ed.publications WHERE comment_count > 369\"\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": ["comment_count", "financial_wellbeing_score"]}], "writes": [{"table": "mart.clicks", "columns": ["comment_count", "financial_wellbeing_score"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO humanitarian_assistance SELECT testtype, budget_amount, is_dessert, author_or_editor FROM arctic_research WHERE testtype > 291\"\n", "labels": {"reads": [{"table": "arctic_research", "columns": ["testtype", "budget_amount", "is_dessert", "author_or_editor"]}], "writes": [{"table": "humanitarian_assistance", "columns": ["testtype", "budget_amount", "is_dessert", "author_or_editor"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"donations2022\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "donations2022", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"mineral_extraction_us\")\nsrc.write.insertInto(\"sponsor_trials\", overwrite=True)\n", "labels": {"reads": [{"table": "mineral_extraction_us", "columns": null}], "writes": [{"table": "sponsor_trials", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT high_temperature, building_name FROM taj_mahal_visitors\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"team\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": ["high_temperature", "building_name"]}], "writes": [{"table": "team", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO equipment_maintenance SELECT consultations, restypename, num_tools, loan_amount FROM member_activity WHERE consultations > 186\"\n", "labels": {"reads": [{"table": "member_activity", "columns": ["consultations", "restypename", "num_tools", "loan_amount"]}], "writes": [{"table": "equipment_maintenance", "columns": ["consultations", "restypename", "num_tools", "loan_amount"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT labor_hour_id, timestamp FROM drought_impact LIMIT 11\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO intelligence_agents SELECT courtid, date_in_locaton_to FROM navalvessels WHERE courtid > 243\")\n", "labels": {"reads": [{"table": "drought_impact", "columns": ["labor_hour_id", "timestamp"]}, {"table": "navalvessels", "columns": ["courtid", "date_in_locaton_to"]}], "writes": [{"table": "intelligence_agents", "columns": ["courtid", "date_in_locaton_to"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO election SELECT problem_log_id, subject_area_id FROM ods.ods_campaigns_hourly WHERE problem_log_id > 41\"\n", "labels": {"reads": [{"table": "ods.ods_campaigns_hourly", "columns": ["problem_log_id", "subject_area_id"]}], "writes": [{"table": "election", "columns": ["problem_log_id", "subject_area_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT inventoryid, total FROM community_development_projects LIMIT 367\")\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO trust SELECT clinic_type, month FROM ads_cart_item_hourly WHERE clinic_type > 337\")\n", "labels": {"reads": [{"table": "community_development_projects", "columns": ["inventoryid", "total"]}, {"table": "ads_cart_item_hourly", "columns": ["clinic_type", "month"]}], "writes": [{"table": "trust", "columns": ["clinic_type", "month"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.biz_date > 481).all()\n# src table: city_properties\nengine.execute(\"INSERT INTO green_energy_lending_programs SELECT * FROM city_properties\")\n", "labels": {"reads": [{"table": "city_properties", "columns": null}], "writes": [{"table": "green_energy_lending_programs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO lives_in SELECT grade, years_working FROM tech_volunteers WHERE grade > 404\");\n", "labels": {"reads": [{"table": "tech_volunteers", "columns": ["grade", "years_working"]}], "writes": [{"table": "lives_in", "columns": ["grade", "years_working"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM product_catalog\", conn)\ndf.to_sql(\"militarycyberops\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "product_catalog", "columns": null}], "writes": [{"table": "militarycyberops", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO spacecraft_temperatures (exit_date, account_balance) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "spacecraft_temperatures", "columns": ["exit_date", "account_balance"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO workerbuildings SELECT jul, assessmentdate FROM doctors WHERE jul > 354\")\n", "labels": {"reads": [{"table": "doctors", "columns": ["jul", "assessmentdate"]}], "writes": [{"table": "workerbuildings", "columns": ["jul", "assessmentdate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO communitypolicingcenters (budget_in_billions, launch_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "communitypolicingcenters", "columns": ["budget_in_billions", "launch_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table initiative_types --target-dir /tmp/land\n", "labels": {"reads": [{"table": "initiative_types", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model clicks_delta depends on ods.ods_users_di\ndbt run --models clicks_delta --vars 'source: ods.ods_users_di'\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": null}], "writes": [{"table": "clicks_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO ods.ods_device_log_delta SELECT track_id, province_id, esg_factor FROM ngo_funding WHERE track_id > 371\");\n", "labels": {"reads": [{"table": "ngo_funding", "columns": ["track_id", "province_id", "esg_factor"]}], "writes": [{"table": "ods.ods_device_log_delta", "columns": ["track_id", "province_id", "esg_factor"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT attraction_id, dock_status FROM dailystreams LIMIT 72\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "dailystreams", "columns": ["attraction_id", "dock_status"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO spacecraft_manufacturers SELECT characteristic_data_type, satellite FROM cyber_incidents WHERE characteristic_data_type > 496\"\n", "labels": {"reads": [{"table": "cyber_incidents", "columns": ["characteristic_data_type", "satellite"]}], "writes": [{"table": "spacecraft_manufacturers", "columns": ["characteristic_data_type", "satellite"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 451;\nEOF\n", "labels": {"reads": [{"table": "indie_artists", "columns": ["shipment_date", "unionid"]}], "writes": [{"table": "player_sessions", "columns": ["shipment_date", "unionid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO expenses SELECT outcome_name, date_incident_end, section_title FROM chemical_concentration WHERE outcome_name > 418\"\n", "labels": {"reads": [{"table": "chemical_concentration", "columns": ["outcome_name", "date_incident_end", "section_title"]}], "writes": [{"table": "expenses", "columns": ["outcome_name", "date_incident_end", "section_title"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"electric_vehicles\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"atlantic_ocean_fish\")\n", "labels": {"reads": [{"table": "electric_vehicles", "columns": null}], "writes": [{"table": "atlantic_ocean_fish", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mediterranean_salinity\"\n", "labels": {"reads": [{"table": "mediterranean_salinity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"online_travel_agency\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "online_travel_agency", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"police_emergencies\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dwd.dwd_risk_score_delta\")\n", "labels": {"reads": [{"table": "police_emergencies", "columns": null}], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthparityviolations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"disasters\")\n", "labels": {"reads": [{"table": "mentalhealthparityviolations", "columns": null}], "writes": [{"table": "disasters", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO recruiters SELECT customer_id, createdate FROM vessel_safety WHERE customer_id > 497\")\n", "labels": {"reads": [{"table": "vessel_safety", "columns": ["customer_id", "createdate"]}], "writes": [{"table": "recruiters", "columns": ["customer_id", "createdate"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"member_of\")\nupsert_to_target(df, \"has_amenity\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "member_of", "columns": null}], "writes": [{"table": "has_amenity", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO school_enrollment SELECT * FROM legacy\nspark.sql(\"INSERT INTO animal_population SELECT union_id, productionid, ll_activity, time_of_purchase FROM fare_collection WHERE union_id > 428\")\n", "labels": {"reads": [{"table": "fare_collection", "columns": ["union_id", "productionid", "ll_activity", "time_of_purchase"]}], "writes": [{"table": "animal_population", "columns": ["union_id", "productionid", "ll_activity", "time_of_purchase"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads_refunds_full\"\n", "labels": {"reads": [{"table": "ads_refunds_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO sanctuaryanimals SELECT signupdate, founding_year, nickname, relationship FROM benefits_overpayments WHERE signupdate > 406\")\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": ["signupdate", "founding_year", "nickname", "relationship"]}], "writes": [{"table": "sanctuaryanimals", "columns": ["signupdate", "founding_year", "nickname", "relationship"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO visitordemographics SELECT energy_id, don_id, countid, host_city_id FROM lead_mines WHERE energy_id > 447\");\n", "labels": {"reads": [{"table": "lead_mines", "columns": ["energy_id", "don_id", "countid", "host_city_id"]}], "writes": [{"table": "visitordemographics", "columns": ["energy_id", "don_id", "countid", "host_city_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO concentrateprices SELECT production_rate, recycled, origin_city, vessel_name FROM yttrium_production WHERE production_rate > 15\");\n", "labels": {"reads": [{"table": "yttrium_production", "columns": ["production_rate", "recycled", "origin_city", "vessel_name"]}], "writes": [{"table": "concentrateprices", "columns": ["production_rate", "recycled", "origin_city", "vessel_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_campaigns\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table item --columns account_id,event_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "item", "columns": ["account_id", "event_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model labor_statistics depends on mentalhealthscores\ndbt build --select labor_statistics --vars '{\"src\":\"mentalhealthscores\"}'\n", "labels": {"reads": [{"table": "mentalhealthscores", "columns": null}], "writes": [{"table": "labor_statistics", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 148;\nEOF\n", "labels": {"reads": [{"table": "dwd.dwd_inventory_hourly", "columns": ["blockfloor", "biomass"]}], "writes": [{"table": "atlantic_ocean_fish", "columns": ["blockfloor", "biomass"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"displaced_people\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "displaced_people", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO freshwater_fish_farms SELECT heritage_site_id, merchandise_id, middle_name FROM virtual_tours WHERE heritage_site_id > 313\");\n", "labels": {"reads": [{"table": "virtual_tours", "columns": ["heritage_site_id", "merchandise_id", "middle_name"]}], "writes": [{"table": "freshwater_fish_farms", "columns": ["heritage_site_id", "merchandise_id", "middle_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws_coupon_use\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.end_date > 145).all()\n# src table: drivers\nengine.execute(\"INSERT INTO veteran_stats SELECT * FROM drivers\")\n", "labels": {"reads": [{"table": "drivers", "columns": null}], "writes": [{"table": "veteran_stats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cuisine\").toPandas()\ndf[[\"taxi_model\", \"order_details\"]].to_sql(\"pets\", engine, index=False)\n", "labels": {"reads": [{"table": "cuisine", "columns": null}], "writes": [{"table": "pets", "columns": ["taxi_model", "order_details"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO culturalcompetencytraining SELECT eia_date, case_burden, home_team FROM socialimpactinvestments WHERE eia_date > 136\")\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": ["eia_date", "case_burden", "home_team"]}], "writes": [{"table": "culturalcompetencytraining", "columns": ["eia_date", "case_burden", "home_team"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bookings (mental_health_resource_access, num_of_audience) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bookings", "columns": ["mental_health_resource_access", "num_of_audience"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO makeup_products SELECT do_value, wins, experienceid, marketing_region_code FROM dws.exposure WHERE do_value > 403\"\n", "labels": {"reads": [{"table": "dws.exposure", "columns": ["do_value", "wins", "experienceid", "marketing_region_code"]}], "writes": [{"table": "makeup_products", "columns": ["do_value", "wins", "experienceid", "marketing_region_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.environmental_impact > 217).all()\n# src table: disaster_zones\nengine.execute(\"INSERT INTO dp_articles SELECT * FROM disaster_zones\")\n", "labels": {"reads": [{"table": "disaster_zones", "columns": null}], "writes": [{"table": "dp_articles", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"european_healthcare\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dw.users_hourly\")\n", "labels": {"reads": [{"table": "european_healthcare", "columns": null}], "writes": [{"table": "dw.users_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT media_outlet, coach_id FROM issues\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"satellite_missions_large\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "issues", "columns": ["media_outlet", "coach_id"]}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"marinespeciesobservations\");\ndf.write().mode(\"overwrite\").saveAsTable(\"paintings\");\n", "labels": {"reads": [{"table": "marinespeciesobservations", "columns": null}], "writes": [{"table": "paintings", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"military_equipment_maintenance\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "military_equipment_maintenance", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"legislation\")\nsrc.write.insertInto(\"communitycenters\", overwrite=True)\n", "labels": {"reads": [{"table": "legislation", "columns": null}], "writes": [{"table": "communitycenters", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ods.ods_risk_score_delta SELECT * FROM legacy\nspark.sql(\"INSERT INTO mental_health_clinics SELECT representative_name, program_category, pid FROM landfillcapacitybycountry WHERE representative_name > 496\")\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": ["representative_name", "program_category", "pid"]}], "writes": [{"table": "mental_health_clinics", "columns": ["representative_name", "program_category", "pid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO factories SELECT assets_billion, count, coach_name FROM athlete_wellbeing WHERE assets_billion > 188\"\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": ["assets_billion", "count", "coach_name"]}], "writes": [{"table": "factories", "columns": ["assets_billion", "count", "coach_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sustainability_fact (waste_amount, community_size) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sustainability_fact", "columns": ["waste_amount", "community_size"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"employee\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "employee", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO refugee_support SELECT building_manager, song_id, community_name FROM intelligence_agency WHERE building_manager > 60\");\n", "labels": {"reads": [{"table": "intelligence_agency", "columns": ["building_manager", "song_id", "community_name"]}], "writes": [{"table": "refugee_support", "columns": ["building_manager", "song_id", "community_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table medals --target-dir /tmp/land\n", "labels": {"reads": [{"table": "medals", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT enddate, acc_type FROM bi.bi_risk_score_full LIMIT 409\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "bi.bi_risk_score_full", "columns": ["enddate", "acc_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bus_fare_collection\");\ndf.write().mode(\"overwrite\").saveAsTable(\"reporters\");\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": null}], "writes": [{"table": "reporters", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"claims_documents\").toPandas()\ndf[[\"certification_id\", \"certification\"]].to_sql(\"co2_sequestration\", engine, index=False)\n", "labels": {"reads": [{"table": "claims_documents", "columns": null}], "writes": [{"table": "co2_sequestration", "columns": ["certification_id", "certification"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.mart_users_di\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"assets\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "assets", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 95;\nSQL\n", "labels": {"reads": [{"table": "water_distribution", "columns": ["heritage_site", "awayteamid"]}, {"table": "vehiclemodels", "columns": ["artworkyear", "pet_age"]}], "writes": [{"table": "bus_fares", "columns": ["artworkyear", "pet_age"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"graduates\");\ndf.write().mode(\"overwrite\").saveAsTable(\"web_client_accelerator\");\n", "labels": {"reads": [{"table": "graduates", "columns": null}], "writes": [{"table": "web_client_accelerator", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO neodymium_prices SELECT sustainabilityrating, ai_id FROM ads.ads_payments WHERE sustainabilityrating > 29\"\n", "labels": {"reads": [{"table": "ads.ads_payments", "columns": ["sustainabilityrating", "ai_id"]}], "writes": [{"table": "neodymium_prices", "columns": ["sustainabilityrating", "ai_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO policy SELECT * FROM legacy\ncur.execute(\"SELECT fleet_id, sustainability_id FROM mining_companies LIMIT 257\")\n", "labels": {"reads": [{"table": "mining_companies", "columns": ["fleet_id", "sustainability_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 114;\nSQL\n", "labels": {"reads": [{"table": "species_observations", "columns": ["stu_dob", "district_name"]}, {"table": "casesbyyear", "columns": ["num_courses", "ai_adoption", "staff_gender", "dockingid"]}], "writes": [{"table": "battery_projects", "columns": ["num_courses", "ai_adoption", "staff_gender", "dockingid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"deep_sea_species\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"authenticationlogs\")\n", "labels": {"reads": [{"table": "deep_sea_species", "columns": null}], "writes": [{"table": "authenticationlogs", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO makeup_products SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT exhibitionname, scan_date FROM autoshows LIMIT 357\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "autoshows", "columns": ["exhibitionname", "scan_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bridgerainfall SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"foodaid\")\nsrc.write.insertInto(\"miningoperations\", overwrite=True)\n", "labels": {"reads": [{"table": "foodaid", "columns": null}], "writes": [{"table": "miningoperations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"store_district\").where(\"dt = current_date()\").writeTo(\"members\").append()\n", "labels": {"reads": [{"table": "store_district", "columns": null}], "writes": [{"table": "members", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.protected > 37).all()\n# src table: categories\nengine.execute(\"INSERT INTO food_assistance SELECT * FROM categories\")\n", "labels": {"reads": [{"table": "categories", "columns": null}], "writes": [{"table": "food_assistance", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"ads.refunds\")\npersist_to_store(df, \"atlantic_ocean\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.refunds", "columns": null}], "writes": [{"table": "atlantic_ocean", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 419;\nSQL\n", "labels": {"reads": [{"table": "courts", "columns": ["running_time", "rid"]}, {"table": "institution", "columns": ["member_name", "county"]}], "writes": [{"table": "ods.ods_risk_score_df", "columns": ["member_name", "county"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO aquatic_species SELECT product_stock_number, service_type_description, faculty_id, staff_id FROM state_contracts WHERE product_stock_number > 187\"\n", "labels": {"reads": [{"table": "state_contracts", "columns": ["product_stock_number", "service_type_description", "faculty_id", "staff_id"]}], "writes": [{"table": "aquatic_species", "columns": ["product_stock_number", "service_type_description", "faculty_id", "staff_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO school_enrollment (character, operation_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "school_enrollment", "columns": ["character", "operation_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO dw.dw_payments_full SELECT province_id, songid FROM dwd.dwd_orders_daily WHERE province_id > 334\")\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": ["province_id", "songid"]}], "writes": [{"table": "dw.dw_payments_full", "columns": ["province_id", "songid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ingredient_sourcing\"\n", "labels": {"reads": [{"table": "ingredient_sourcing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"tokyo_motor_show\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "tokyo_motor_show", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"volume\");\ndf.write().mode(\"overwrite\").saveAsTable(\"investors\");\n", "labels": {"reads": [{"table": "volume", "columns": null}], "writes": [{"table": "investors", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"bikerental\")\nsrc.write.insertInto(\"article_views\", overwrite=True)\n", "labels": {"reads": [{"table": "bikerental", "columns": null}], "writes": [{"table": "article_views", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"appellations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"seafoodsouthafricakenya\")\n", "labels": {"reads": [{"table": "appellations", "columns": null}], "writes": [{"table": "seafoodsouthafricakenya", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"solar_farms\");\ndf.write().mode(\"overwrite\").saveAsTable(\"biosensors.projects\");\n", "labels": {"reads": [{"table": "solar_farms", "columns": null}], "writes": [{"table": "biosensors.projects", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO inspections (other_item_details, enable_third_party_ads) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "inspections", "columns": ["other_item_details", "enable_third_party_ads"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.stg_inventory_hourly SELECT * FROM legacy\ncur.execute(\"SELECT crop_name, preference_rating FROM restaurants LIMIT 341\")\n", "labels": {"reads": [{"table": "restaurants", "columns": ["crop_name", "preference_rating"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.mart_products_hourly (seal_species, maintenancedate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_products_hourly", "columns": ["seal_species", "maintenancedate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO indie_artists SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"fertilizer_usage\").where(\"dt = current_date()\").writeTo(\"track\").append()\n", "labels": {"reads": [{"table": "fertilizer_usage", "columns": null}], "writes": [{"table": "track", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 198;\nSQL\n", "labels": {"reads": [{"table": "veterans", "columns": ["production_cost", "away_team_score"]}, {"table": "donations2022", "columns": ["biomass", "date_opened", "president_vote"]}], "writes": [{"table": "stg.stg_inventory_full", "columns": ["biomass", "date_opened", "president_vote"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.engineer_id > 92).all()\n# src table: coal\nengine.execute(\"INSERT INTO test_drives SELECT * FROM coal\")\n", "labels": {"reads": [{"table": "coal", "columns": null}], "writes": [{"table": "test_drives", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO veteran_unemployment (content_id, launch_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "veteran_unemployment", "columns": ["content_id", "launch_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"wildlife\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"directors\")\n", "labels": {"reads": [{"table": "wildlife", "columns": null}], "writes": [{"table": "directors", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO race_ethnicity SELECT location_name, resolutiondate FROM waterconservationinitiatives WHERE location_name > 367\");\n", "labels": {"reads": [{"table": "waterconservationinitiatives", "columns": ["location_name", "resolutiondate"]}], "writes": [{"table": "race_ethnicity", "columns": ["location_name", "resolutiondate"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO recycling_stats SELECT cultivatorname, pallet_id, portid, height_feet FROM military_tech WHERE cultivatorname > 43\")\n", "labels": {"reads": [{"table": "military_tech", "columns": ["cultivatorname", "pallet_id", "portid", "height_feet"]}], "writes": [{"table": "recycling_stats", "columns": ["cultivatorname", "pallet_id", "portid", "height_feet"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"document_sections_images\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws.shipments_daily\");\n", "labels": {"reads": [{"table": "document_sections_images", "columns": null}], "writes": [{"table": "dws.shipments_daily", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"virtual_tour_stats\");\ndf.write().mode(\"overwrite\").saveAsTable(\"threats\");\n", "labels": {"reads": [{"table": "virtual_tour_stats", "columns": null}], "writes": [{"table": "threats", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"manufacturermaterials\").where(\"dt = current_date()\").writeTo(\"military_personnel_africa\").append()\n", "labels": {"reads": [{"table": "manufacturermaterials", "columns": null}], "writes": [{"table": "military_personnel_africa", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"gamestats\");\ndf.write().mode(\"overwrite\").saveAsTable(\"timed_status_of_things\");\n", "labels": {"reads": [{"table": "gamestats", "columns": null}], "writes": [{"table": "timed_status_of_things", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"user\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"rainfall_data\")\n", "labels": {"reads": [{"table": "user", "columns": null}], "writes": [{"table": "rainfall_data", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sustainable_urban\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "sustainable_urban", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO ticket_sales SELECT energy_star_rating, studentname, lawyer_name, vaccine_type FROM menu WHERE energy_star_rating > 395\"\n", "labels": {"reads": [{"table": "menu", "columns": ["energy_star_rating", "studentname", "lawyer_name", "vaccine_type"]}], "writes": [{"table": "ticket_sales", "columns": ["energy_star_rating", "studentname", "lawyer_name", "vaccine_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO student_access SELECT a.employer_organisation_id, b.draft_class FROM stg.stg_events_di a JOIN legal_precedents b ON a.policy_type_code = b.policy_type_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": null}, {"table": "legal_precedents", "columns": null}], "writes": [{"table": "student_access", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"phone_market\");\ndf.write().mode(\"overwrite\").saveAsTable(\"recyclingcenters\");\n", "labels": {"reads": [{"table": "phone_market", "columns": null}], "writes": [{"table": "recyclingcenters", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO readership SELECT journalist_id, vrdevice, is_operational, country_code FROM biosensors.projects WHERE journalist_id > 28\");\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": ["journalist_id", "vrdevice", "is_operational", "country_code"]}], "writes": [{"table": "readership", "columns": ["journalist_id", "vrdevice", "is_operational", "country_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO bi.bi_orders_daily SELECT accessibility, portname, farmland_id FROM mart_refunds_delta WHERE accessibility > 391\"\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": ["accessibility", "portname", "farmland_id"]}], "writes": [{"table": "bi.bi_orders_daily", "columns": ["accessibility", "portname", "farmland_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT is_vegan, artifacttype FROM tracklists LIMIT 474\")\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO product_sales SELECT petid, transact_date, installed_date FROM government.region WHERE petid > 16\")\n", "labels": {"reads": [{"table": "tracklists", "columns": ["is_vegan", "artifacttype"]}, {"table": "government.region", "columns": ["petid", "transact_date", "installed_date"]}], "writes": [{"table": "product_sales", "columns": ["petid", "transact_date", "installed_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"artistsdemographics\");\ndf.write().mode(\"overwrite\").saveAsTable(\"bi.users_full\");\n", "labels": {"reads": [{"table": "artistsdemographics", "columns": null}], "writes": [{"table": "bi.users_full", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO transport (away_team_id, workers) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "transport", "columns": ["away_team_id", "workers"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"otas\").toPandas()\ndf[[\"production_usage\", \"billing_country\"]].to_sql(\"ads\", engine, index=False)\n", "labels": {"reads": [{"table": "otas", "columns": null}], "writes": [{"table": "ads", "columns": ["production_usage", "billing_country"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO communication_scores SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO measurements SELECT violation_type, plan_type FROM education_programs WHERE violation_type > 431\");\n", "labels": {"reads": [{"table": "education_programs", "columns": ["violation_type", "plan_type"]}], "writes": [{"table": "measurements", "columns": ["violation_type", "plan_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT council_id, teacher_id FROM florida_conservation_initiatives LIMIT 92\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "florida_conservation_initiatives", "columns": ["council_id", "teacher_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 431;\nEOF\n", "labels": {"reads": [{"table": "stg.campaigns_daily", "columns": ["sustainable", "marketing_region_name", "orgname", "bicycle_id"]}], "writes": [{"table": "exoplanet_discoveries", "columns": ["sustainable", "marketing_region_name", "orgname", "bicycle_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"weather\").where(\"dt = current_date()\").writeTo(\"autonomousdriving\").append()\n", "labels": {"reads": [{"table": "weather", "columns": null}], "writes": [{"table": "autonomousdriving", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fertilizer_usage\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "fertilizer_usage", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 323;\nEOF\n", "labels": {"reads": [{"table": "artist", "columns": ["line_1_number_building", "game_count", "invoice_date"]}], "writes": [{"table": "programs", "columns": ["line_1_number_building", "game_count", "invoice_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM departments\"\n", "labels": {"reads": [{"table": "departments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO results SELECT zone_name, master_customer_id FROM traffic WHERE zone_name > 370\"], check=True)\n", "labels": {"reads": [{"table": "traffic", "columns": ["zone_name", "master_customer_id"]}], "writes": [{"table": "results", "columns": ["zone_name", "master_customer_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO files SELECT max_salary, headquarters, is_public, pass_fail FROM dws.dws_risk_score_df WHERE max_salary > 275\");\n", "labels": {"reads": [{"table": "dws.dws_risk_score_df", "columns": ["max_salary", "headquarters", "is_public", "pass_fail"]}], "writes": [{"table": "files", "columns": ["max_salary", "headquarters", "is_public", "pass_fail"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"manager_award\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"recycledmaterialsgarments\")\n", "labels": {"reads": [{"table": "manager_award", "columns": null}], "writes": [{"table": "recycledmaterialsgarments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nsql = \"INSERT INTO artprograms SELECT a.state_id, b.kills FROM members a JOIN status b ON a.therapy_sessions = b.therapy_sessions\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "members", "columns": null}, {"table": "status", "columns": null}], "writes": [{"table": "artprograms", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mammals\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"aus_wellbeing\")\n", "labels": {"reads": [{"table": "mammals", "columns": null}], "writes": [{"table": "aus_wellbeing", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table renewables.renewable_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "renewables.renewable_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"buildings\")\nsrc.write.insertInto(\"sourcing\", overwrite=True)\n", "labels": {"reads": [{"table": "buildings", "columns": null}], "writes": [{"table": "sourcing", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 224;\nEOF\n", "labels": {"reads": [{"table": "social_good_projects", "columns": ["constructorid", "profits_in_billion", "sid", "inclusive"]}], "writes": [{"table": "mammals", "columns": ["constructorid", "profits_in_billion", "sid", "inclusive"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.ods_sessions_df\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"art_pieces\")\n", "labels": {"reads": [{"table": "ods.ods_sessions_df", "columns": null}], "writes": [{"table": "art_pieces", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO workforcediversity SELECT rating, numhearings FROM mine_workforce WHERE rating > 225\"\n", "labels": {"reads": [{"table": "mine_workforce", "columns": ["rating", "numhearings"]}], "writes": [{"table": "workforcediversity", "columns": ["rating", "numhearings"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO unions SELECT soil_moisture, num_cases FROM mart_events_full WHERE soil_moisture > 379\")\n", "labels": {"reads": [{"table": "mart_events_full", "columns": ["soil_moisture", "num_cases"]}], "writes": [{"table": "unions", "columns": ["soil_moisture", "num_cases"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table heritagesites --columns mediatorid,away_team_score --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "heritagesites", "columns": ["mediatorid", "away_team_score"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO airlines SELECT * FROM legacy\nspark.sql(\"INSERT INTO disaster_zones SELECT theme, ship_agent_id FROM flight_safety WHERE theme > 123\")\n", "labels": {"reads": [{"table": "flight_safety", "columns": ["theme", "ship_agent_id"]}], "writes": [{"table": "disaster_zones", "columns": ["theme", "ship_agent_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.value_points > 61).all()\n# src table: customersregion\nengine.execute(\"INSERT INTO founder SELECT * FROM customersregion\")\n", "labels": {"reads": [{"table": "customersregion", "columns": null}], "writes": [{"table": "founder", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table match_result --columns coach_name,official_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "match_result", "columns": ["coach_name", "official_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 159;\nEOF\n", "labels": {"reads": [{"table": "circular_supply_chain_products", "columns": ["sensor_type", "customerid"]}], "writes": [{"table": "rural_projects", "columns": ["sensor_type", "customerid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"red_line\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"sustainable_practices\")\n", "labels": {"reads": [{"table": "red_line", "columns": null}], "writes": [{"table": "sustainable_practices", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO electoral_register SELECT aircraft, hiredate, unit, floors FROM excavation_sites WHERE aircraft > 219\"\n", "labels": {"reads": [{"table": "excavation_sites", "columns": ["aircraft", "hiredate", "unit", "floors"]}], "writes": [{"table": "electoral_register", "columns": ["aircraft", "hiredate", "unit", "floors"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.job > 454).all()\n# src table: media_library\nengine.execute(\"INSERT INTO track SELECT * FROM media_library\")\n", "labels": {"reads": [{"table": "media_library", "columns": null}], "writes": [{"table": "track", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"game_scores\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"rural_economy_2\")\n", "labels": {"reads": [{"table": "game_scores", "columns": null}], "writes": [{"table": "rural_economy_2", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO user_stats SELECT claim_status_description, contact_staff_id, pets_allowed_yn, crs_description FROM team WHERE claim_status_description > 162\"\n", "labels": {"reads": [{"table": "team", "columns": ["claim_status_description", "contact_staff_id", "pets_allowed_yn", "crs_description"]}], "writes": [{"table": "user_stats", "columns": ["claim_status_description", "contact_staff_id", "pets_allowed_yn", "crs_description"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"view_unit_status\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"smart_contracts_table\")\n", "labels": {"reads": [{"table": "view_unit_status", "columns": null}], "writes": [{"table": "smart_contracts_table", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 327;\nSQL\n", "labels": {"reads": [{"table": "fabrics", "columns": ["hospital_name", "restaurant_id"]}, {"table": "regions", "columns": ["providerid", "issue_date"]}], "writes": [{"table": "royal_family", "columns": ["providerid", "issue_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO rural_clinics SELECT call_time, f_id, scan_date FROM energy_efficiency_projects WHERE call_time > 40\"\n", "labels": {"reads": [{"table": "energy_efficiency_projects", "columns": ["call_time", "f_id", "scan_date"]}], "writes": [{"table": "rural_clinics", "columns": ["call_time", "f_id", "scan_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model revenue depends on tourism\ndbt build --models revenue --vars 'source: tourism'\n", "labels": {"reads": [{"table": "tourism", "columns": null}], "writes": [{"table": "revenue", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"daily_production\").toPandas()\ndf[[\"draft_details\", \"patient_id\"]].to_sql(\"stg.stg_users\", engine, index=False)\n", "labels": {"reads": [{"table": "daily_production", "columns": null}], "writes": [{"table": "stg.stg_users", "columns": ["draft_details", "patient_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO document_sections_images SELECT race_ethnicity, budget FROM taj_mahal_visitors WHERE race_ethnicity > 131\"\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": ["race_ethnicity", "budget"]}], "writes": [{"table": "document_sections_images", "columns": ["race_ethnicity", "budget"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO marine_life_research SELECT trade, component_type, event_id FROM wastewater_treatment_plants WHERE trade > 140\"\n", "labels": {"reads": [{"table": "wastewater_treatment_plants", "columns": ["trade", "component_type", "event_id"]}], "writes": [{"table": "marine_life_research", "columns": ["trade", "component_type", "event_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.stg_events_di SELECT * FROM legacy\ncur.execute(\"SELECT yield_per_acre, population FROM ods.ods_users_di LIMIT 430\")\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": ["yield_per_acre", "population"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vessel SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO flight_emissions SELECT a.low_temperature, b.num_songs FROM activities a JOIN marine_species_observations b ON a.cost = b.cost\"\n", "labels": {"reads": [{"table": "activities", "columns": null}, {"table": "marine_species_observations", "columns": null}], "writes": [{"table": "flight_emissions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dorm\").toPandas()\ndf[[\"exploited\", \"image_date\"]].to_sql(\"farms\", engine, index=False)\n", "labels": {"reads": [{"table": "dorm", "columns": null}], "writes": [{"table": "farms", "columns": ["exploited", "image_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"animal_population_status\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"suburbs\")\n", "labels": {"reads": [{"table": "animal_population_status", "columns": null}], "writes": [{"table": "suburbs", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"opioid_overdoses\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "opioid_overdoses", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO fairness_scores SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"opioid_overdoses\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "opioid_overdoses", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table recyclers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "recyclers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"stations\")\nsrc.write.insertInto(\"cloud_issues\", overwrite=True)\n", "labels": {"reads": [{"table": "stations", "columns": null}], "writes": [{"table": "cloud_issues", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mental_health_parity\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mental_health_parity", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"inclusive_housing\").where(\"dt = current_date()\").writeTo(\"dwd.exposure_hourly\").append()\n", "labels": {"reads": [{"table": "inclusive_housing", "columns": null}], "writes": [{"table": "dwd.exposure_hourly", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dws_products\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dws_products", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cinema SELECT claim_type, stockid, survey_id FROM ports WHERE claim_type > 92\"\n", "labels": {"reads": [{"table": "ports", "columns": ["claim_type", "stockid", "survey_id"]}], "writes": [{"table": "cinema", "columns": ["claim_type", "stockid", "survey_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO inspectiondata SELECT co_id, completion_date FROM movie WHERE co_id > 429\"\n", "labels": {"reads": [{"table": "movie", "columns": ["co_id", "completion_date"]}], "writes": [{"table": "inspectiondata", "columns": ["co_id", "completion_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO companies_extended SELECT courses, lot_details, affected_population, statename FROM animal_population WHERE courses > 484\");\n", "labels": {"reads": [{"table": "animal_population", "columns": ["courses", "lot_details", "affected_population", "statename"]}], "writes": [{"table": "companies_extended", "columns": ["courses", "lot_details", "affected_population", "statename"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO web_client_accelerator SELECT document_type_code, mountain_id, recipient_id FROM rental WHERE document_type_code > 257\")\n", "labels": {"reads": [{"table": "rental", "columns": ["document_type_code", "mountain_id", "recipient_id"]}], "writes": [{"table": "web_client_accelerator", "columns": ["document_type_code", "mountain_id", "recipient_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO investment_rounds (tourist_id, propertyid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "investment_rounds", "columns": ["tourist_id", "propertyid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model enrollments depends on menu_engineering\ndbt build -s enrollments --vars '{\"src\":\"menu_engineering\"}'\n", "labels": {"reads": [{"table": "menu_engineering", "columns": null}], "writes": [{"table": "enrollments", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fossil_fuel_vehicles\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"sports\")\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": null}], "writes": [{"table": "sports", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO forest SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO tv_shows SELECT co2_reduction_tons, height_feet, technology, zone_id FROM community_engagement WHERE co2_reduction_tons > 44\")\n", "labels": {"reads": [{"table": "community_engagement", "columns": ["co2_reduction_tons", "height_feet", "technology", "zone_id"]}], "writes": [{"table": "tv_shows", "columns": ["co2_reduction_tons", "height_feet", "technology", "zone_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT long, component_type FROM news_stories LIMIT 139\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "news_stories", "columns": ["long", "component_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO climate_adaptation_projects SELECT * FROM legacy\ncur.execute(\"SELECT personnelid, destinationid FROM circular_economy_companies LIMIT 499\")\n", "labels": {"reads": [{"table": "circular_economy_companies", "columns": ["personnelid", "destinationid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO safety_incidents_india SELECT report_id, coupon_id, organization_details, eia_date FROM shelters WHERE report_id > 165\"\n", "labels": {"reads": [{"table": "shelters", "columns": ["report_id", "coupon_id", "organization_details", "eia_date"]}], "writes": [{"table": "safety_incidents_india", "columns": ["report_id", "coupon_id", "organization_details", "eia_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ca_menu_items\", conn)\ndf.to_sql(\"travel_advisory\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": null}], "writes": [{"table": "travel_advisory", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 206;\nSQL\n", "labels": {"reads": [{"table": "manufacturing_processes", "columns": ["job_title", "effort_name"]}, {"table": "climate_monitoring_stations", "columns": ["initiative_type", "dock_id", "institution_id"]}], "writes": [{"table": "ads.ads_member_point_daily", "columns": ["initiative_type", "dock_id", "institution_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO employeepromotions SELECT service_details, invoice_date, component_name, attraction_name FROM language WHERE service_details > 391\"\n", "labels": {"reads": [{"table": "language", "columns": ["service_details", "invoice_date", "component_name", "attraction_name"]}], "writes": [{"table": "employeepromotions", "columns": ["service_details", "invoice_date", "component_name", "attraction_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 298;\nEOF\n", "labels": {"reads": [{"table": "music_streaming", "columns": ["permitid", "aid_name", "numcases"]}], "writes": [{"table": "dwd.dwd_payments_di", "columns": ["permitid", "aid_name", "numcases"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"university\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "university", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO excavation SELECT * FROM legacy\ncur.execute(\"SELECT teacher_id, subscription_start_date FROM dwd.dwd_orders_di LIMIT 228\")\n", "labels": {"reads": [{"table": "dwd.dwd_orders_di", "columns": ["teacher_id", "subscription_start_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.suburb > 74).all()\n# src table: models_safety\nengine.execute(\"INSERT INTO chemicals SELECT * FROM models_safety\")\n", "labels": {"reads": [{"table": "models_safety", "columns": null}], "writes": [{"table": "chemicals", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO user_ad_interactions (farm_id, booking_start_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "user_ad_interactions", "columns": ["farm_id", "booking_start_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tb_cases\", conn)\ndf.to_sql(\"stg_orders_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tb_cases", "columns": null}], "writes": [{"table": "stg_orders_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO disaster_mitigation SELECT date_of_latest_revision, founder_gender FROM posts_per_day WHERE date_of_latest_revision > 308\")\n", "labels": {"reads": [{"table": "posts_per_day", "columns": ["date_of_latest_revision", "founder_gender"]}], "writes": [{"table": "disaster_mitigation", "columns": ["date_of_latest_revision", "founder_gender"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"zipcodes\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"race\")\n", "labels": {"reads": [{"table": "zipcodes", "columns": null}], "writes": [{"table": "race", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO user_video_view SELECT unit_of_measure, num_accessible_tech_centers, date_incident_end FROM community_policing WHERE unit_of_measure > 448\")\n", "labels": {"reads": [{"table": "community_policing", "columns": ["unit_of_measure", "num_accessible_tech_centers", "date_incident_end"]}], "writes": [{"table": "user_video_view", "columns": ["unit_of_measure", "num_accessible_tech_centers", "date_incident_end"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO wrestler SELECT card_number, union_name, cell_mobile_phone_number FROM prices WHERE card_number > 428\");\n", "labels": {"reads": [{"table": "prices", "columns": ["card_number", "union_name", "cell_mobile_phone_number"]}], "writes": [{"table": "wrestler", "columns": ["card_number", "union_name", "cell_mobile_phone_number"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM authorship\", conn)\ndf.to_sql(\"artworksales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "authorship", "columns": null}], "writes": [{"table": "artworksales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO geneva_motor_show SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO assignedto SELECT eventdate, productionrate, effort_name, budget_allocated FROM ads.risk_score WHERE eventdate > 317\"\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": ["eventdate", "productionrate", "effort_name", "budget_allocated"]}], "writes": [{"table": "assignedto", "columns": ["eventdate", "productionrate", "effort_name", "budget_allocated"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO bi.bi_risk_score_delta SELECT animal_id, founded, grant_type FROM mine_workforce WHERE animal_id > 240\");\n", "labels": {"reads": [{"table": "mine_workforce", "columns": ["animal_id", "founded", "grant_type"]}], "writes": [{"table": "bi.bi_risk_score_delta", "columns": ["animal_id", "founded", "grant_type"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 94;\nEOF\n", "labels": {"reads": [{"table": "fabricdata", "columns": ["lat", "user_name", "is_operational"]}], "writes": [{"table": "electric_vehicle_stats", "columns": ["lat", "user_name", "is_operational"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ref_locations --columns open_year,professionalid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ref_locations", "columns": ["open_year", "professionalid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"trainings\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"materials_usage\")\n", "labels": {"reads": [{"table": "trainings", "columns": null}], "writes": [{"table": "materials_usage", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO payments SELECT character, budget_allocation, rooms FROM renewable_power WHERE character > 139\")\n", "labels": {"reads": [{"table": "renewable_power", "columns": ["character", "budget_allocation", "rooms"]}], "writes": [{"table": "payments", "columns": ["character", "budget_allocation", "rooms"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"fairtradecertifications\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "fairtradecertifications", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"haircare_cruelty\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "haircare_cruelty", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO resilience_infrastructure SELECT instid, trip_city, playerid, problem_description FROM atlantic_ocean WHERE instid > 15\")\n", "labels": {"reads": [{"table": "atlantic_ocean", "columns": ["instid", "trip_city", "playerid", "problem_description"]}], "writes": [{"table": "resilience_infrastructure", "columns": ["instid", "trip_city", "playerid", "problem_description"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contractor_name, reservoir_id FROM government.region LIMIT 102\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO community_centers SELECT has_spf, first_year, home_team FROM dws.dws_inventory_hourly WHERE has_spf > 217\")\n", "labels": {"reads": [{"table": "government.region", "columns": ["contractor_name", "reservoir_id"]}, {"table": "dws.dws_inventory_hourly", "columns": ["has_spf", "first_year", "home_team"]}], "writes": [{"table": "community_centers", "columns": ["has_spf", "first_year", "home_team"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO drought_data SELECT timestamp, competition FROM esports_teams WHERE timestamp > 374\");\n", "labels": {"reads": [{"table": "esports_teams", "columns": ["timestamp", "competition"]}], "writes": [{"table": "drought_data", "columns": ["timestamp", "competition"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO programs SELECT funding_amount, vehicleid, meal_name FROM climate_monitoring_stations WHERE funding_amount > 285\");\n", "labels": {"reads": [{"table": "climate_monitoring_stations", "columns": ["funding_amount", "vehicleid", "meal_name"]}], "writes": [{"table": "programs", "columns": ["funding_amount", "vehicleid", "meal_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO smartcitytech SELECT resource_name, fish_count, founder_identity, apt_type_code FROM plots WHERE resource_name > 191\")\n", "labels": {"reads": [{"table": "plots", "columns": ["resource_name", "fish_count", "founder_identity", "apt_type_code"]}], "writes": [{"table": "smartcitytech", "columns": ["resource_name", "fish_count", "founder_identity", "apt_type_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_life_populations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"biosensors\")\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": null}], "writes": [{"table": "biosensors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"rural_hospitals\");\ndf.write().mode(\"overwrite\").saveAsTable(\"peakhours\");\n", "labels": {"reads": [{"table": "rural_hospitals", "columns": null}], "writes": [{"table": "peakhours", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO time_dim SELECT bioprocess_name, dept_name, working_year_starts, water_temp FROM dws.inventory_daily WHERE bioprocess_name > 371\"\n", "labels": {"reads": [{"table": "dws.inventory_daily", "columns": ["bioprocess_name", "dept_name", "working_year_starts", "water_temp"]}], "writes": [{"table": "time_dim", "columns": ["bioprocess_name", "dept_name", "working_year_starts", "water_temp"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT staff_details, assets FROM ads.ads_member_point_daily\", engine)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"bus_fare_collection\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_member_point_daily", "columns": ["staff_details", "assets"]}], "writes": [{"table": "bus_fare_collection", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO video_content SELECT ocean, emp_fname, network FROM exhibitions WHERE ocean > 260\"], check=True)\n", "labels": {"reads": [{"table": "exhibitions", "columns": ["ocean", "emp_fname", "network"]}], "writes": [{"table": "video_content", "columns": ["ocean", "emp_fname", "network"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM threat_intelligence_budget\", conn)\ndf.to_sql(\"user_likes\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "threat_intelligence_budget", "columns": null}], "writes": [{"table": "user_likes", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"researchpapers\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "researchpapers", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM prices\", conn)\ndf.to_sql(\"trainmaintenance\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "prices", "columns": null}], "writes": [{"table": "trainmaintenance", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO teachers SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO shariah_compliant_finance SELECT ethical_manufacturing, away_team_three_point, low_income_neighborhood FROM organisations WHERE ethical_manufacturing > 418\")\n", "labels": {"reads": [{"table": "organisations", "columns": ["ethical_manufacturing", "away_team_three_point", "low_income_neighborhood"]}], "writes": [{"table": "shariah_compliant_finance", "columns": ["ethical_manufacturing", "away_team_three_point", "low_income_neighborhood"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table strandings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "strandings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO record SELECT event_type_id, technique_id FROM stations WHERE event_type_id > 114\")\n", "labels": {"reads": [{"table": "stations", "columns": ["event_type_id", "technique_id"]}], "writes": [{"table": "record", "columns": ["event_type_id", "technique_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 432;\nSQL\n", "labels": {"reads": [{"table": "stg_payments_hourly", "columns": ["hoursperweek", "founding_location"]}, {"table": "teachers", "columns": ["vaccinations", "archeologist", "gamegenre", "lanes"]}], "writes": [{"table": "stg_users_daily", "columns": ["vaccinations", "archeologist", "gamegenre", "lanes"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO inclusivehousing.affordablehousing SELECT a.year_join, b.traveler_id FROM mining_companies a JOIN creativeais b ON a.inventor_name = b.inventor_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mining_companies", "columns": null}, {"table": "creativeais", "columns": null}], "writes": [{"table": "inclusivehousing.affordablehousing", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO store_district SELECT amount_paid, site_id, total_horses FROM product_info WHERE amount_paid > 475\"], check=True)\n", "labels": {"reads": [{"table": "product_info", "columns": ["amount_paid", "site_id", "total_horses"]}], "writes": [{"table": "store_district", "columns": ["amount_paid", "site_id", "total_horses"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO investments SELECT report_id, vote_percent FROM dispensarysales WHERE report_id > 250\"\n", "labels": {"reads": [{"table": "dispensarysales", "columns": ["report_id", "vote_percent"]}], "writes": [{"table": "investments", "columns": ["report_id", "vote_percent"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM customer_month\"\n", "labels": {"reads": [{"table": "customer_month", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO artistsales SELECT to_address, start, end_time, salesperson FROM soilmoisturedata WHERE to_address > 290\"\n", "labels": {"reads": [{"table": "soilmoisturedata", "columns": ["to_address", "start", "end_time", "salesperson"]}], "writes": [{"table": "artistsales", "columns": ["to_address", "start", "end_time", "salesperson"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"highways\");\ndf.write().mode(\"overwrite\").saveAsTable(\"labor_cost\");\n", "labels": {"reads": [{"table": "highways", "columns": null}], "writes": [{"table": "labor_cost", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO mart.device_log_hourly SELECT nid, investment_name FROM judges WHERE nid > 411\"\n", "labels": {"reads": [{"table": "judges", "columns": ["nid", "investment_name"]}], "writes": [{"table": "mart.device_log_hourly", "columns": ["nid", "investment_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO acidification_data SELECT * FROM legacy\ncur.execute(\"SELECT adoption_date, family_name FROM rigs LIMIT 398\")\n", "labels": {"reads": [{"table": "rigs", "columns": ["adoption_date", "family_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO educationprograms SELECT condition, updated_at FROM ads.refunds_delta WHERE condition > 360\"\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": ["condition", "updated_at"]}], "writes": [{"table": "educationprograms", "columns": ["condition", "updated_at"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"weather\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "weather", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO dancefunding SELECT nominee, subject_id, issue_month, followers FROM transportation_per_country WHERE nominee > 303\");\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": ["nominee", "subject_id", "issue_month", "followers"]}], "writes": [{"table": "dancefunding", "columns": ["nominee", "subject_id", "issue_month", "followers"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO military_expenditure SELECT pipeline_name, brand_mentioned FROM mobile_usage WHERE pipeline_name > 376\");\n", "labels": {"reads": [{"table": "mobile_usage", "columns": ["pipeline_name", "brand_mentioned"]}], "writes": [{"table": "military_expenditure", "columns": ["pipeline_name", "brand_mentioned"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO sites SELECT * FROM legacy\nspark.sql(\"INSERT INTO mart.mart_users SELECT astronaut_name, state_id, round FROM block WHERE astronaut_name > 317\")\n", "labels": {"reads": [{"table": "block", "columns": ["astronaut_name", "state_id", "round"]}], "writes": [{"table": "mart.mart_users", "columns": ["astronaut_name", "state_id", "round"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"budgets\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"communityengagement\")\n", "labels": {"reads": [{"table": "budgets", "columns": null}], "writes": [{"table": "communityengagement", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table tech_workers_union --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"investor_activities\").where(\"dt = current_date()\").writeTo(\"restaurant\").append()\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": [{"table": "restaurant", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"order_items\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "order_items", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.campaigns_df SELECT a.inclusive_housing_policy, b.founded FROM mart.mart_sessions_di a JOIN parts b ON a.nominee = b.nominee\"\n", "labels": {"reads": [{"table": "mart.mart_sessions_di", "columns": null}, {"table": "parts", "columns": null}], "writes": [{"table": "stg.campaigns_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO donors_region SELECT discovered_date, ai_model, publicationid FROM defense_spending WHERE discovered_date > 464\"\n", "labels": {"reads": [{"table": "defense_spending", "columns": ["discovered_date", "ai_model", "publicationid"]}], "writes": [{"table": "donors_region", "columns": ["discovered_date", "ai_model", "publicationid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO trade_history SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"employeepromotions\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "employeepromotions", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO ods.sessions SELECT date_of_transaction, rank_in_round FROM dysprosium_mines WHERE date_of_transaction > 137\")\n", "labels": {"reads": [{"table": "dysprosium_mines", "columns": ["date_of_transaction", "rank_in_round"]}], "writes": [{"table": "ods.sessions", "columns": ["date_of_transaction", "rank_in_round"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO high_risk SELECT chw_id, grant_amount, main_services, fda_approved FROM historicalcontexts WHERE chw_id > 57\");\n", "labels": {"reads": [{"table": "historicalcontexts", "columns": ["chw_id", "grant_amount", "main_services", "fda_approved"]}], "writes": [{"table": "high_risk", "columns": ["chw_id", "grant_amount", "main_services", "fda_approved"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO class SELECT speed, musical_id, membername, trip_city FROM singer_in_concert WHERE speed > 348\"\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": ["speed", "musical_id", "membername", "trip_city"]}], "writes": [{"table": "class", "columns": ["speed", "musical_id", "membername", "trip_city"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artwork (date_and_date, treatment) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artwork", "columns": ["date_and_date", "treatment"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mental_health_professionals_2 SELECT * FROM legacy\ncur.execute(\"SELECT number_cities, premise_id FROM dwd.sessions LIMIT 463\")\n", "labels": {"reads": [{"table": "dwd.sessions", "columns": ["number_cities", "premise_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 481;\nEOF\n", "labels": {"reads": [{"table": "subscribers", "columns": ["isfirstattendee", "wifi", "attack_id", "organization"]}], "writes": [{"table": "investmentsesg", "columns": ["isfirstattendee", "wifi", "attack_id", "organization"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO employee_demographics SELECT * FROM legacy\nspark.sql(\"INSERT INTO inst SELECT postal_code, submission_id, crime_date FROM projecttimelinebybudget WHERE postal_code > 454\")\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": ["postal_code", "submission_id", "crime_date"]}], "writes": [{"table": "inst", "columns": ["postal_code", "submission_id", "crime_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO athletes SELECT assists, tourist_attraction_id FROM electricvehicleadoption WHERE assists > 477\")\n", "labels": {"reads": [{"table": "electricvehicleadoption", "columns": ["assists", "tourist_attraction_id"]}], "writes": [{"table": "athletes", "columns": ["assists", "tourist_attraction_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO clothingsales SELECT status, strainid, playtime FROM diseases WHERE status > 364\"\n", "labels": {"reads": [{"table": "diseases", "columns": ["status", "strainid", "playtime"]}], "writes": [{"table": "clothingsales", "columns": ["status", "strainid", "playtime"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads.ads_inventory_df SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM arcticocean\", conn)\ndf.to_sql(\"satisfaction\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}], "writes": [{"table": "satisfaction", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO restaurant_type SELECT product_category_code, organic_ingredients_percentage, sustainabilityrating, granteeid FROM atlantic_plate WHERE product_category_code > 480\"\n", "labels": {"reads": [{"table": "atlantic_plate", "columns": ["product_category_code", "organic_ingredients_percentage", "sustainabilityrating", "granteeid"]}], "writes": [{"table": "restaurant_type", "columns": ["product_category_code", "organic_ingredients_percentage", "sustainabilityrating", "granteeid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"makeup_products\").toPandas()\ndf[[\"product_type_code\", \"requestdate\"]].to_sql(\"view_unit_status\", engine, index=False)\n", "labels": {"reads": [{"table": "makeup_products", "columns": null}], "writes": [{"table": "view_unit_status", "columns": ["product_type_code", "requestdate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"investment_rounds\").where(\"dt = current_date()\").writeTo(\"investment\").append()\n", "labels": {"reads": [{"table": "investment_rounds", "columns": null}], "writes": [{"table": "investment", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO apartment_bookings SELECT * FROM legacy\ncur.execute(\"SELECT initiativeid, orderdate FROM dwd.dwd_exposure_full LIMIT 227\")\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_full", "columns": ["initiativeid", "orderdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table ota_revenue --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ota_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO events SELECT spacecraft_name, college_id, release_date FROM restaurant_type WHERE spacecraft_name > 316\"\n", "labels": {"reads": [{"table": "restaurant_type", "columns": ["spacecraft_name", "college_id", "release_date"]}], "writes": [{"table": "events", "columns": ["spacecraft_name", "college_id", "release_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mining_companies SELECT creationyear, destroyed_by_employee_id FROM mineral_extraction WHERE creationyear > 485\"], check=True)\n", "labels": {"reads": [{"table": "mineral_extraction", "columns": ["creationyear", "destroyed_by_employee_id"]}], "writes": [{"table": "mining_companies", "columns": ["creationyear", "destroyed_by_employee_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO community_education_programs SELECT organization_id, operationid FROM courts WHERE organization_id > 30\"\n", "labels": {"reads": [{"table": "courts", "columns": ["organization_id", "operationid"]}], "writes": [{"table": "community_education_programs", "columns": ["organization_id", "operationid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO member_of SELECT * FROM legacy\nspark.sql(\"INSERT INTO collective_bargaining SELECT ingredient, contract_amount, vulnerability_name FROM astronaut_medical_3 WHERE ingredient > 393\")\n", "labels": {"reads": [{"table": "astronaut_medical_3", "columns": ["ingredient", "contract_amount", "vulnerability_name"]}], "writes": [{"table": "collective_bargaining", "columns": ["ingredient", "contract_amount", "vulnerability_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO teacher_development_race SELECT mineid, component_name, incident_date FROM bi.risk_score_df WHERE mineid > 230\"\n", "labels": {"reads": [{"table": "bi.risk_score_df", "columns": ["mineid", "component_name", "incident_date"]}], "writes": [{"table": "teacher_development_race", "columns": ["mineid", "component_name", "incident_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO social_good_education SELECT * FROM legacy\ncur.execute(\"SELECT paintingid, years_played FROM marine_mammals LIMIT 301\")\n", "labels": {"reads": [{"table": "marine_mammals", "columns": ["paintingid", "years_played"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"event_attendance\");\ndf.write().mode(\"overwrite\").saveAsTable(\"music\");\n", "labels": {"reads": [{"table": "event_attendance", "columns": null}], "writes": [{"table": "music", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT received_date, period FROM station LIMIT 352\")\nimport logging\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO drama_workshop_groups SELECT account_type, building_description, dysprosium_prod FROM sustainableprojects WHERE account_type > 14\")\n", "labels": {"reads": [{"table": "station", "columns": ["received_date", "period"]}, {"table": "sustainableprojects", "columns": ["account_type", "building_description", "dysprosium_prod"]}], "writes": [{"table": "drama_workshop_groups", "columns": ["account_type", "building_description", "dysprosium_prod"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"instructors\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"militaryinnovations\")\n", "labels": {"reads": [{"table": "instructors", "columns": null}], "writes": [{"table": "militaryinnovations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hospitalname > 311).all()\n# src table: recyclers\nengine.execute(\"INSERT INTO policy_feedback SELECT * FROM recyclers\")\n", "labels": {"reads": [{"table": "recyclers", "columns": null}], "writes": [{"table": "policy_feedback", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO culturalcompetencytrainings SELECT division, dept_store_id FROM licenses WHERE division > 183\"\n", "labels": {"reads": [{"table": "licenses", "columns": ["division", "dept_store_id"]}], "writes": [{"table": "culturalcompetencytrainings", "columns": ["division", "dept_store_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO defenseprojects SELECT scan_date, visitorid, voter_id, account_details FROM training_programs WHERE scan_date > 104\"\n", "labels": {"reads": [{"table": "training_programs", "columns": ["scan_date", "visitorid", "voter_id", "account_details"]}], "writes": [{"table": "defenseprojects", "columns": ["scan_date", "visitorid", "voter_id", "account_details"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM invoices\", conn)\ndf.to_sql(\"militarydrones\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "invoices", "columns": null}], "writes": [{"table": "militarydrones", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_exposure_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart_vendors_full\")\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_full", "columns": null}], "writes": [{"table": "mart_vendors_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO building_permits SELECT wellbeing_score, opened_date, violationid, province FROM shared_rides_tokyo WHERE wellbeing_score > 118\");\n", "labels": {"reads": [{"table": "shared_rides_tokyo", "columns": ["wellbeing_score", "opened_date", "violationid", "province"]}], "writes": [{"table": "building_permits", "columns": ["wellbeing_score", "opened_date", "violationid", "province"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"immunization\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "immunization", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ma_inspections (material_type, accreditation_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ma_inspections", "columns": ["material_type", "accreditation_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cosmetic_formula SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO news_report SELECT sustainability_id, city_area, g_name FROM claims WHERE sustainability_id > 486\")\n", "labels": {"reads": [{"table": "claims", "columns": ["sustainability_id", "city_area", "g_name"]}], "writes": [{"table": "news_report", "columns": ["sustainability_id", "city_area", "g_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO legal_precedents SELECT porphyria, chemical, museum_details FROM researchpapers WHERE porphyria > 189\"\n", "labels": {"reads": [{"table": "researchpapers", "columns": ["porphyria", "chemical", "museum_details"]}], "writes": [{"table": "legal_precedents", "columns": ["porphyria", "chemical", "museum_details"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.customer_id > 246).all()\n# src table: winter_olympics\nengine.execute(\"INSERT INTO warehouses SELECT * FROM winter_olympics\")\n", "labels": {"reads": [{"table": "winter_olympics", "columns": null}], "writes": [{"table": "warehouses", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO dws.dws_events_hourly SELECT billing_state, budget, hire_date, accreditation_type FROM medicine_enzyme_interaction WHERE billing_state > 78\");\n", "labels": {"reads": [{"table": "medicine_enzyme_interaction", "columns": ["billing_state", "budget", "hire_date", "accreditation_type"]}], "writes": [{"table": "dws.dws_events_hourly", "columns": ["billing_state", "budget", "hire_date", "accreditation_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO tryout SELECT billingcountry, authorder, medical_risk FROM volunteerprograms WHERE billingcountry > 205\"\n", "labels": {"reads": [{"table": "volunteerprograms", "columns": ["billingcountry", "authorder", "medical_risk"]}], "writes": [{"table": "tryout", "columns": ["billingcountry", "authorder", "medical_risk"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 135;\nSQL\n", "labels": {"reads": [{"table": "safetyorgs", "columns": ["farm_name", "rating_in_percent"]}, {"table": "ods_shipments_df", "columns": ["system_id", "statement_details", "ethical_certifications"]}], "writes": [{"table": "crime_stats", "columns": ["system_id", "statement_details", "ethical_certifications"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donorgender\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bi.users_full\")\n", "labels": {"reads": [{"table": "donorgender", "columns": null}], "writes": [{"table": "bi.users_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dw_users_full SELECT a.nominee, b.text_of_notes FROM middle_east_military_spending a JOIN eco_diversification_investment b ON a.subscriber_id = b.subscriber_id\"\n", "labels": {"reads": [{"table": "middle_east_military_spending", "columns": null}, {"table": "eco_diversification_investment", "columns": null}], "writes": [{"table": "dw_users_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model mart_coupon_use_full depends on fish_biomass\ndbt run --models mart_coupon_use_full --vars '{\"source_table\":\"fish_biomass\"}'\n", "labels": {"reads": [{"table": "fish_biomass", "columns": null}], "writes": [{"table": "mart_coupon_use_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO topublictransportation SELECT worker_count, hours_spent FROM fish_purchases WHERE worker_count > 262\");\n", "labels": {"reads": [{"table": "fish_purchases", "columns": ["worker_count", "hours_spent"]}], "writes": [{"table": "topublictransportation", "columns": ["worker_count", "hours_spent"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.max_age > 305).all()\n# src table: ocean_health_monitor\nengine.execute(\"INSERT INTO london.stations SELECT * FROM ocean_health_monitor\")\n", "labels": {"reads": [{"table": "ocean_health_monitor", "columns": null}], "writes": [{"table": "london.stations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"assets\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"multimodal_trips\")\n", "labels": {"reads": [{"table": "assets", "columns": null}], "writes": [{"table": "multimodal_trips", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fabrics\").toPandas()\ndf[[\"spacecraft_model\", \"response_received_date\"]].to_sql(\"bi.bi_orders_delta\", engine, index=False)\n", "labels": {"reads": [{"table": "fabrics", "columns": null}], "writes": [{"table": "bi.bi_orders_delta", "columns": ["spacecraft_model", "response_received_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table space_debris --target-dir /tmp/land\n", "labels": {"reads": [{"table": "space_debris", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ods.ods_users_daily\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"offender_demographics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"agricultural_innovations\")\n", "labels": {"reads": [{"table": "offender_demographics", "columns": null}], "writes": [{"table": "agricultural_innovations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO companies SELECT event_location, coverage_type FROM device_usage WHERE event_location > 231\")\n", "labels": {"reads": [{"table": "device_usage", "columns": ["event_location", "coverage_type"]}], "writes": [{"table": "companies", "columns": ["event_location", "coverage_type"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO inclusion_efforts SELECT date_of_latest_logon, missionid, investor_name, menuitem FROM contract_transactions WHERE date_of_latest_logon > 127\"\n", "labels": {"reads": [{"table": "contract_transactions", "columns": ["date_of_latest_logon", "missionid", "investor_name", "menuitem"]}], "writes": [{"table": "inclusion_efforts", "columns": ["date_of_latest_logon", "missionid", "investor_name", "menuitem"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws_cart_item SELECT depth, chemical, conferencename FROM individual WHERE depth > 6\"], check=True)\n", "labels": {"reads": [{"table": "individual", "columns": ["depth", "chemical", "conferencename"]}], "writes": [{"table": "dws_cart_item", "columns": ["depth", "chemical", "conferencename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 52;\nSQL\n", "labels": {"reads": [{"table": "communitydevelopment", "columns": ["capacity_mw", "mine_name"]}, {"table": "journal", "columns": ["lot_id", "complaintid", "exhibitioncountry"]}], "writes": [{"table": "public.ev_sales", "columns": ["lot_id", "complaintid", "exhibitioncountry"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO agricultural_innovations SELECT a.supplier, b.route FROM grapes a JOIN city_tech b ON a.watertemp = b.watertemp\"\n", "labels": {"reads": [{"table": "grapes", "columns": null}, {"table": "city_tech", "columns": null}], "writes": [{"table": "agricultural_innovations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"volunteers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"check_ins\")\n", "labels": {"reads": [{"table": "volunteers", "columns": null}], "writes": [{"table": "check_ins", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO rent_arrears SELECT a.characteristic_id, b.clientid FROM circuits a JOIN stg_orders_hourly b ON a.founder = b.founder\"\n", "labels": {"reads": [{"table": "circuits", "columns": null}, {"table": "stg_orders_hourly", "columns": null}], "writes": [{"table": "rent_arrears", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO marine_life_research SELECT leader_name, strat_id, electoral_register_id FROM phone WHERE leader_name > 263\");\n", "labels": {"reads": [{"table": "phone", "columns": ["leader_name", "strat_id", "electoral_register_id"]}], "writes": [{"table": "marine_life_research", "columns": ["leader_name", "strat_id", "electoral_register_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mobile_usage\").where(\"dt = current_date()\").writeTo(\"restaurants_tx\").append()\n", "labels": {"reads": [{"table": "mobile_usage", "columns": null}], "writes": [{"table": "restaurants_tx", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO operations SELECT hometown, purchase_id, online_dispute_resolution, mean_temperature_f FROM vessels_2 WHERE hometown > 104\")\n", "labels": {"reads": [{"table": "vessels_2", "columns": ["hometown", "purchase_id", "online_dispute_resolution", "mean_temperature_f"]}], "writes": [{"table": "operations", "columns": ["hometown", "purchase_id", "online_dispute_resolution", "mean_temperature_f"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table restaurant --target-dir /tmp/land\n", "labels": {"reads": [{"table": "restaurant", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO follows SELECT premises_type, address_content, item FROM suppliersfairlabor WHERE premises_type > 309\")\n", "labels": {"reads": [{"table": "suppliersfairlabor", "columns": ["premises_type", "address_content", "item"]}], "writes": [{"table": "follows", "columns": ["premises_type", "address_content", "item"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT therapy_type, co2_reduction FROM shrimp_farms\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"stg.stg_campaigns_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": ["therapy_type", "co2_reduction"]}], "writes": [{"table": "stg.stg_campaigns_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_input(ctx, \"stations\")\ndump_to_output(df, \"art\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stations", "columns": null}], "writes": [{"table": "art", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"epl_teams\").toPandas()\ndf[[\"thefttypeid\", \"contactid\"]].to_sql(\"gender\", engine, index=False)\n", "labels": {"reads": [{"table": "epl_teams", "columns": null}], "writes": [{"table": "gender", "columns": ["thefttypeid", "contactid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO climate_investments SELECT price, account_number, report FROM creativeais WHERE price > 325\");\n", "labels": {"reads": [{"table": "creativeais", "columns": ["price", "account_number", "report"]}], "writes": [{"table": "climate_investments", "columns": ["price", "account_number", "report"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO products SELECT lesson_status_code, order_id, invoice_id FROM agriculturalinnovations WHERE lesson_status_code > 4\"], check=True)\n", "labels": {"reads": [{"table": "agriculturalinnovations", "columns": ["lesson_status_code", "order_id", "invoice_id"]}], "writes": [{"table": "products", "columns": ["lesson_status_code", "order_id", "invoice_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 128;\nSQL\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": ["retailer_name", "country"]}, {"table": "bi_refunds_daily", "columns": ["dose", "balance", "enddate"]}], "writes": [{"table": "inclusive_housing", "columns": ["dose", "balance", "enddate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO co_ownership (implemented_date, trip_start_time) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "co_ownership", "columns": ["implemented_date", "trip_start_time"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"landfill_capacity_north_america\");\ndf.write().mode(\"overwrite\").saveAsTable(\"vessel_capacity\");\n", "labels": {"reads": [{"table": "landfill_capacity_north_america", "columns": null}], "writes": [{"table": "vessel_capacity", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO city_waste_generation SELECT journalist_id, dates_active, investors, show_id FROM marine_life_sightings WHERE journalist_id > 439\")\n", "labels": {"reads": [{"table": "marine_life_sightings", "columns": ["journalist_id", "dates_active", "investors", "show_id"]}], "writes": [{"table": "city_waste_generation", "columns": ["journalist_id", "dates_active", "investors", "show_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO lots SELECT consider_rate, stuid FROM public_participation WHERE consider_rate > 122\"\n", "labels": {"reads": [{"table": "public_participation", "columns": ["consider_rate", "stuid"]}], "writes": [{"table": "lots", "columns": ["consider_rate", "stuid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO public.trips_by_day_train SELECT restaurantid, spacecraft, country_of_origin, attribute_id FROM drilling_rigs WHERE restaurantid > 295\")\n", "labels": {"reads": [{"table": "drilling_rigs", "columns": ["restaurantid", "spacecraft", "country_of_origin", "attribute_id"]}], "writes": [{"table": "public.trips_by_day_train", "columns": ["restaurantid", "spacecraft", "country_of_origin", "attribute_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO lessons SELECT line_id, author_community, county_name, zipcode FROM vessels WHERE line_id > 352\"], check=True)\n", "labels": {"reads": [{"table": "vessels", "columns": ["line_id", "author_community", "county_name", "zipcode"]}], "writes": [{"table": "lessons", "columns": ["line_id", "author_community", "county_name", "zipcode"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO multimodalhubs SELECT attorney, donor_name FROM art_exhibit_attendance WHERE attorney > 229\")\n", "labels": {"reads": [{"table": "art_exhibit_attendance", "columns": ["attorney", "donor_name"]}], "writes": [{"table": "multimodalhubs", "columns": ["attorney", "donor_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO num_employees SELECT visit_id, citizen_id FROM flight_safety WHERE visit_id > 80\");\n", "labels": {"reads": [{"table": "flight_safety", "columns": ["visit_id", "citizen_id"]}], "writes": [{"table": "num_employees", "columns": ["visit_id", "citizen_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"continent\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dw.dw_inventory_delta\");\n", "labels": {"reads": [{"table": "continent", "columns": null}], "writes": [{"table": "dw.dw_inventory_delta", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ads.ads_vendors_hourly\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ods_member_point_full\");\n", "labels": {"reads": [{"table": "ads.ads_vendors_hourly", "columns": null}], "writes": [{"table": "ods_member_point_full", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM danceevents\", conn)\ndf.to_sql(\"textile_waste\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "danceevents", "columns": null}], "writes": [{"table": "textile_waste", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO battery_storage SELECT a.sustainable, b.tech_id FROM zipcodes a JOIN virtual_tour_engagement b ON a.line_number = b.line_number\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "zipcodes", "columns": null}, {"table": "virtual_tour_engagement", "columns": null}], "writes": [{"table": "battery_storage", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM investment_strategies\", conn)\ndf.to_sql(\"materials\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "investment_strategies", "columns": null}], "writes": [{"table": "materials", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO contract_negotiations_un SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"soil_moisture\")\nsink_to_sink(df, \"creativeais\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "soil_moisture", "columns": null}], "writes": [{"table": "creativeais", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dw.events_hourly SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT wellname, water_depth FROM artifact_analysis LIMIT 85\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO water_usage SELECT workshop_name, manufacturerid, industry_4_0, date_complaint_raised FROM producers WHERE workshop_name > 135\")\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": ["wellname", "water_depth"]}, {"table": "producers", "columns": ["workshop_name", "manufacturerid", "industry_4_0", "date_complaint_raised"]}], "writes": [{"table": "water_usage", "columns": ["workshop_name", "manufacturerid", "industry_4_0", "date_complaint_raised"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.inventory_daily (heart_rate, policy_type_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.inventory_daily", "columns": ["heart_rate", "policy_type_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"milestones\")\nsink_to_store(df, \"community_events\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "milestones", "columns": null}], "writes": [{"table": "community_events", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 218;\nEOF\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles_japan", "columns": ["stu_hrs", "country_name", "subject_name", "stu_fname"]}], "writes": [{"table": "rural.bus_trips", "columns": ["stu_hrs", "country_name", "subject_name", "stu_fname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO menuitems (eventdate, audienceid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "menuitems", "columns": ["eventdate", "audienceid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO station_company SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO military_innovation SELECT sodium, strain_id FROM fairtradefactories WHERE sodium > 310\")\n", "labels": {"reads": [{"table": "fairtradefactories", "columns": ["sodium", "strain_id"]}], "writes": [{"table": "military_innovation", "columns": ["sodium", "strain_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO policy_advocacy SELECT * FROM legacy\ncur.execute(\"SELECT time_id, strat_id FROM daily_articles_by_category LIMIT 181\")\n", "labels": {"reads": [{"table": "daily_articles_by_category", "columns": ["time_id", "strat_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.cart_item_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"pediatricians\")\n", "labels": {"reads": [{"table": "stg.cart_item_full", "columns": null}], "writes": [{"table": "pediatricians", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"electricvehicles\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "electricvehicles", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO colorado_river_basin SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vrusers (workers, master_customer_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vrusers", "columns": ["workers", "master_customer_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO irrigation_systems SELECT budgeted, artifactid, start_time, vr_platform FROM faculty WHERE budgeted > 228\")\n", "labels": {"reads": [{"table": "faculty", "columns": ["budgeted", "artifactid", "start_time", "vr_platform"]}], "writes": [{"table": "irrigation_systems", "columns": ["budgeted", "artifactid", "start_time", "vr_platform"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT characteristic_data_type, organic_matter FROM patient LIMIT 115\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "patient", "columns": ["characteristic_data_type", "organic_matter"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"electric_buses\").where(\"dt = current_date()\").writeTo(\"co2_sequestration\").append()\n", "labels": {"reads": [{"table": "electric_buses", "columns": null}], "writes": [{"table": "co2_sequestration", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO product_characteristics SELECT train_number, contract_date, founder_country, next_entry_id FROM legalaidrequests WHERE train_number > 475\"\n", "labels": {"reads": [{"table": "legalaidrequests", "columns": ["train_number", "contract_date", "founder_country", "next_entry_id"]}], "writes": [{"table": "product_characteristics", "columns": ["train_number", "contract_date", "founder_country", "next_entry_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.stg_risk_score SELECT community_name, rig_id, killed FROM royal_family WHERE community_name > 128\"\n", "labels": {"reads": [{"table": "royal_family", "columns": ["community_name", "rig_id", "killed"]}], "writes": [{"table": "stg.stg_risk_score", "columns": ["community_name", "rig_id", "killed"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"geothermal_power_plants\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"player_sessions\")\n", "labels": {"reads": [{"table": "geothermal_power_plants", "columns": null}], "writes": [{"table": "player_sessions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT bedroom_count, event_type_id FROM article_views LIMIT 377\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "article_views", "columns": ["bedroom_count", "event_type_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO courts SELECT sport, cases_count, safety_rating FROM residents_services WHERE sport > 122\"\n", "labels": {"reads": [{"table": "residents_services", "columns": ["sport", "cases_count", "safety_rating"]}], "writes": [{"table": "courts", "columns": ["sport", "cases_count", "safety_rating"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_master_index\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"disease_prevalence\")\n", "labels": {"reads": [{"table": "customer_master_index", "columns": null}], "writes": [{"table": "disease_prevalence", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"performance_scores\").toPandas()\ndf[[\"playdate\", \"ship_agent_id\"]].to_sql(\"sitem\", engine, index=False)\n", "labels": {"reads": [{"table": "performance_scores", "columns": null}], "writes": [{"table": "sitem", "columns": ["playdate", "ship_agent_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM esports_teams\"\n", "labels": {"reads": [{"table": "esports_teams", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO dates SELECT total_amount, destroyed_by_employee_id, launch_date FROM dp_articles WHERE total_amount > 153\")\n", "labels": {"reads": [{"table": "dp_articles", "columns": ["total_amount", "destroyed_by_employee_id", "launch_date"]}], "writes": [{"table": "dates", "columns": ["total_amount", "destroyed_by_employee_id", "launch_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO satisfaction SELECT open_date, focal_length_mm, co2_emissions FROM performingartsprograms WHERE open_date > 359\");\n", "labels": {"reads": [{"table": "performingartsprograms", "columns": ["open_date", "focal_length_mm", "co2_emissions"]}], "writes": [{"table": "satisfaction", "columns": ["open_date", "focal_length_mm", "co2_emissions"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO files SELECT receipt_date, bookings FROM patient_outcomes WHERE receipt_date > 439\");\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": ["receipt_date", "bookings"]}], "writes": [{"table": "files", "columns": ["receipt_date", "bookings"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO parts SELECT a.container_count, b.period FROM jobs a JOIN farms b ON a.meter_300 = b.meter_300\"\n", "labels": {"reads": [{"table": "jobs", "columns": null}, {"table": "farms", "columns": null}], "writes": [{"table": "parts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.events_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"market_share\")\n", "labels": {"reads": [{"table": "dw.events_hourly", "columns": null}], "writes": [{"table": "market_share", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO autonomous_testing SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO electricvehicles SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO medical_professionals SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"eu_data_usage\").where(\"dt = current_date()\").writeTo(\"autonomous_research\").append()\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": null}], "writes": [{"table": "autonomous_research", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT appointment_duration, delegate FROM fairtradefactories LIMIT 240\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "fairtradefactories", "columns": ["appointment_duration", "delegate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table military_technology_projects --columns meal_name,client_first_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "military_technology_projects", "columns": ["meal_name", "client_first_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"stg.stg_products_full\")\nsrc.write.insertInto(\"train_lines\", overwrite=True)\n", "labels": {"reads": [{"table": "stg.stg_products_full", "columns": null}], "writes": [{"table": "train_lines", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM hydro_plants\", conn)\ndf.to_sql(\"files\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "hydro_plants", "columns": null}], "writes": [{"table": "files", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO marine_life_research SELECT a.building_name, b.trip_start_time FROM harvest_permits a JOIN apac_hotel_views b ON a.tonnage = b.tonnage\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "harvest_permits", "columns": null}, {"table": "apac_hotel_views", "columns": null}], "writes": [{"table": "marine_life_research", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_ethics_policies\").toPandas()\ndf[[\"spacecraft\", \"speciesid\"]].to_sql(\"community_health_center\", engine, index=False)\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": null}], "writes": [{"table": "community_health_center", "columns": ["spacecraft", "speciesid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO mars_spacecraft SELECT recipe_id, farm_id, workshop_group_id, screen_mode FROM cargo_equipment WHERE recipe_id > 271\"\n", "labels": {"reads": [{"table": "cargo_equipment", "columns": ["recipe_id", "farm_id", "workshop_group_id", "screen_mode"]}], "writes": [{"table": "mars_spacecraft", "columns": ["recipe_id", "farm_id", "workshop_group_id", "screen_mode"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO game_sales SELECT unit_id, statement_id FROM animals WHERE unit_id > 207\");\n", "labels": {"reads": [{"table": "animals", "columns": ["unit_id", "statement_id"]}], "writes": [{"table": "game_sales", "columns": ["unit_id", "statement_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO ocean SELECT satelliteid, acidity FROM disaster_zones WHERE satelliteid > 153\");\n", "labels": {"reads": [{"table": "disaster_zones", "columns": ["satelliteid", "acidity"]}], "writes": [{"table": "ocean", "columns": ["satelliteid", "acidity"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.race_ethnicity > 299).all()\n# src table: block\nengine.execute(\"INSERT INTO stg.risk_score_hourly SELECT * FROM block\")\n", "labels": {"reads": [{"table": "block", "columns": null}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO fertilizer_usage SELECT document_type_code, call_date FROM permian_basin WHERE document_type_code > 276\");\n", "labels": {"reads": [{"table": "permian_basin", "columns": ["document_type_code", "call_date"]}], "writes": [{"table": "fertilizer_usage", "columns": ["document_type_code", "call_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO precipitation_data SELECT a.foreign, b.origin_city FROM railway a JOIN dws_events_df b ON a.representative_name = b.representative_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "railway", "columns": null}, {"table": "dws_events_df", "columns": null}], "writes": [{"table": "precipitation_data", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"social_impact_bonds\")\nsrc.write.insertInto(\"teachers\", overwrite=True)\n", "labels": {"reads": [{"table": "social_impact_bonds", "columns": null}], "writes": [{"table": "teachers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fault_log\").toPandas()\ndf[[\"diplomacy_id\", \"citation_time\"]].to_sql(\"researchpapers\", engine, index=False)\n", "labels": {"reads": [{"table": "fault_log", "columns": null}], "writes": [{"table": "researchpapers", "columns": ["diplomacy_id", "citation_time"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT propertyid, artifactid FROM video_content LIMIT 425\")\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO field_production SELECT cargo_weight, laborproductivity, transaction_amount FROM vr_tech WHERE cargo_weight > 399\")\n", "labels": {"reads": [{"table": "video_content", "columns": ["propertyid", "artifactid"]}, {"table": "vr_tech", "columns": ["cargo_weight", "laborproductivity", "transaction_amount"]}], "writes": [{"table": "field_production", "columns": ["cargo_weight", "laborproductivity", "transaction_amount"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO arrivals SELECT * FROM legacy\ncur.execute(\"SELECT species_name, investor_id FROM criminal_justice_reform_initiatives LIMIT 270\")\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": ["species_name", "investor_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO film_market_estimation SELECT * FROM legacy\nspark.sql(\"INSERT INTO stg.orders_daily SELECT mhw_id, sales_amount, ride_id, donationdate FROM bi.payments_daily WHERE mhw_id > 409\")\n", "labels": {"reads": [{"table": "bi.payments_daily", "columns": ["mhw_id", "sales_amount", "ride_id", "donationdate"]}], "writes": [{"table": "stg.orders_daily", "columns": ["mhw_id", "sales_amount", "ride_id", "donationdate"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.recycling_rate > 241).all()\n# src table: hotel_tech_adoption\nengine.execute(\"INSERT INTO tourismproviders SELECT * FROM hotel_tech_adoption\")\n", "labels": {"reads": [{"table": "hotel_tech_adoption", "columns": null}], "writes": [{"table": "tourismproviders", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO gamedata SELECT lawyer_name, subject_name, start, review_score FROM recreation_centers WHERE lawyer_name > 29\"\n", "labels": {"reads": [{"table": "recreation_centers", "columns": ["lawyer_name", "subject_name", "start", "review_score"]}], "writes": [{"table": "gamedata", "columns": ["lawyer_name", "subject_name", "start", "review_score"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 327;\nEOF\n", "labels": {"reads": [{"table": "school_enrollment", "columns": ["dishid", "passengers", "animal", "sculpture_name"]}], "writes": [{"table": "agencies", "columns": ["dishid", "passengers", "animal", "sculpture_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO volunteers SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model orgdonations depends on minor_in\ndbt build --models orgdonations --vars '{\"source_table\":\"minor_in\"}'\n", "labels": {"reads": [{"table": "minor_in", "columns": null}], "writes": [{"table": "orgdonations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO eco_diversification_investment SELECT employment_id, strainname, machinery_id, grant_type FROM dispensarysales WHERE employment_id > 107\")\n", "labels": {"reads": [{"table": "dispensarysales", "columns": ["employment_id", "strainname", "machinery_id", "grant_type"]}], "writes": [{"table": "eco_diversification_investment", "columns": ["employment_id", "strainname", "machinery_id", "grant_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sale_country, nominee FROM ods_member_point_full\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"trip\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods_member_point_full", "columns": ["sale_country", "nominee"]}], "writes": [{"table": "trip", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.partner_id > 354).all()\n# src table: african_union_countries\nengine.execute(\"INSERT INTO researchgrants SELECT * FROM african_union_countries\")\n", "labels": {"reads": [{"table": "african_union_countries", "columns": null}], "writes": [{"table": "researchgrants", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO film_market_estimation SELECT catalog_entry_id, classid FROM wedding WHERE catalog_entry_id > 112\");\n", "labels": {"reads": [{"table": "wedding", "columns": ["catalog_entry_id", "classid"]}], "writes": [{"table": "film_market_estimation", "columns": ["catalog_entry_id", "classid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table elimination --target-dir /tmp/land\n", "labels": {"reads": [{"table": "elimination", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO customer SELECT reports_to, discount FROM staff_members WHERE reports_to > 159\"\n", "labels": {"reads": [{"table": "staff_members", "columns": ["reports_to", "discount"]}], "writes": [{"table": "customer", "columns": ["reports_to", "discount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"eventdates\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.refunds_hourly\")\n", "labels": {"reads": [{"table": "eventdates", "columns": null}], "writes": [{"table": "stg.refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table aid_missions --target-dir /tmp/land\n", "labels": {"reads": [{"table": "aid_missions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO temperatureanomalies SELECT a.policytype, b.cuisine_name FROM employeepromotions a JOIN garments b ON a.galleryid = b.galleryid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "employeepromotions", "columns": null}, {"table": "garments", "columns": null}], "writes": [{"table": "temperatureanomalies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO animal_species (donationyear, time_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "animal_species", "columns": ["donationyear", "time_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 371;\nSQL\n", "labels": {"reads": [{"table": "impact_investments", "columns": ["initiativename", "therapist_id"]}, {"table": "donationhistory", "columns": ["party_phone", "policyholderid", "environmental_impact_score"]}], "writes": [{"table": "museums", "columns": ["party_phone", "policyholderid", "environmental_impact_score"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"participants\").where(\"dt = current_date()\").writeTo(\"fare_segments\").append()\n", "labels": {"reads": [{"table": "participants", "columns": null}], "writes": [{"table": "fare_segments", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"complaints\")\nsave_to_store(df, \"defense_project_timelines\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "complaints", "columns": null}], "writes": [{"table": "defense_project_timelines", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model carbon_prices depends on artwork\ndbt build --models carbon_prices --vars '{\"source_table\":\"artwork\"}'\n", "labels": {"reads": [{"table": "artwork", "columns": null}], "writes": [{"table": "carbon_prices", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model broadband_plans depends on city_labor_cost\ndbt build --select broadband_plans --vars 'source: city_labor_cost'\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": null}], "writes": [{"table": "broadband_plans", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"professional_development\");\ndf.write().mode(\"overwrite\").saveAsTable(\"southchinasea.wells\");\n", "labels": {"reads": [{"table": "professional_development", "columns": null}], "writes": [{"table": "southchinasea.wells", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"musical\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "musical", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO runs SELECT driver_id, studio_name FROM talent_acquisition WHERE driver_id > 439\");\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": ["driver_id", "studio_name"]}], "writes": [{"table": "runs", "columns": ["driver_id", "studio_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.mart_shipments_hourly SELECT * FROM legacy\ncur.execute(\"SELECT birthday, emissions FROM bi.products_daily LIMIT 381\")\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": ["birthday", "emissions"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO degrees SELECT machine_id, trial_name, rows FROM production_sites WHERE machine_id > 498\");\n", "labels": {"reads": [{"table": "production_sites", "columns": ["machine_id", "trial_name", "rows"]}], "writes": [{"table": "degrees", "columns": ["machine_id", "trial_name", "rows"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"offender_demographics\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "offender_demographics", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO imagery_archive SELECT a.line_1_number_building, b.assists FROM timbersales a JOIN rebounds b ON a.playlist_id = b.playlist_id\"\n", "labels": {"reads": [{"table": "timbersales", "columns": null}, {"table": "rebounds", "columns": null}], "writes": [{"table": "imagery_archive", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT billing_country, id FROM tourismproviders LIMIT 493\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "tourismproviders", "columns": ["billing_country", "id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"heritagesites\");\ndf.write().mode(\"overwrite\").saveAsTable(\"sustainableprojects\");\n", "labels": {"reads": [{"table": "heritagesites", "columns": null}], "writes": [{"table": "sustainableprojects", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"purchase\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "purchase", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ads.risk_score SELECT * FROM legacy\nspark.sql(\"INSERT INTO tours SELECT total_amount_purchased, department_id, experiment_name, percentage_change FROM ods_member_point_full WHERE total_amount_purchased > 227\")\n", "labels": {"reads": [{"table": "ods_member_point_full", "columns": ["total_amount_purchased", "department_id", "experiment_name", "percentage_change"]}], "writes": [{"table": "tours", "columns": ["total_amount_purchased", "department_id", "experiment_name", "percentage_change"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.orders_daily SELECT exhibitionid, fault_description, investment FROM areas WHERE exhibitionid > 11\"], check=True)\n", "labels": {"reads": [{"table": "areas", "columns": ["exhibitionid", "fault_description", "investment"]}], "writes": [{"table": "stg.orders_daily", "columns": ["exhibitionid", "fault_description", "investment"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"communityengagementmetrics\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "communityengagementmetrics", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO test_drives SELECT sales_transaction_id, theatrename, productionid FROM state_budget WHERE sales_transaction_id > 493\")\n", "labels": {"reads": [{"table": "state_budget", "columns": ["sales_transaction_id", "theatrename", "productionid"]}], "writes": [{"table": "test_drives", "columns": ["sales_transaction_id", "theatrename", "productionid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"habitats\");\ndf.write().mode(\"overwrite\").saveAsTable(\"tours\");\n", "labels": {"reads": [{"table": "habitats", "columns": null}], "writes": [{"table": "tours", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"org_comms\").toPandas()\ndf[[\"recorded_by_staff_id\", \"employee_name\"]].to_sql(\"dws.cart_item_full\", engine, index=False)\n", "labels": {"reads": [{"table": "org_comms", "columns": null}], "writes": [{"table": "dws.cart_item_full", "columns": ["recorded_by_staff_id", "employee_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM renewable_projects\"\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO user SELECT tree_species, budget_amount, pricepergram FROM school WHERE tree_species > 300\");\n", "labels": {"reads": [{"table": "school", "columns": ["tree_species", "budget_amount", "pricepergram"]}], "writes": [{"table": "user", "columns": ["tree_species", "budget_amount", "pricepergram"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO attribute_definitions SELECT development_name, ticket_subject FROM platformstats WHERE development_name > 1\"\n", "labels": {"reads": [{"table": "platformstats", "columns": ["development_name", "ticket_subject"]}], "writes": [{"table": "attribute_definitions", "columns": ["development_name", "ticket_subject"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO climate_adaptation_projects SELECT employeeid, contract_value, date_incident_end, tour_type FROM tb_reports WHERE employeeid > 305\"\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["employeeid", "contract_value", "date_incident_end", "tour_type"]}], "writes": [{"table": "climate_adaptation_projects", "columns": ["employeeid", "contract_value", "date_incident_end", "tour_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"platformstats\")\nsink_to_store(df, \"disabilitysupportprograms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "platformstats", "columns": null}], "writes": [{"table": "disabilitysupportprograms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO biosensors.projects SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO renewable_projects SELECT menu_type, cost, ship_id FROM rural_hospitals WHERE menu_type > 168\"\n", "labels": {"reads": [{"table": "rural_hospitals", "columns": ["menu_type", "cost", "ship_id"]}], "writes": [{"table": "renewable_projects", "columns": ["menu_type", "cost", "ship_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"policy_feedback\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"africa_schema.african_mines\")\n", "labels": {"reads": [{"table": "policy_feedback", "columns": null}], "writes": [{"table": "africa_schema.african_mines", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"investor_activities\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model platformh depends on economic_diversification\ndbt run -s platformh --vars '{\"source_table\":\"economic_diversification\"}'\n", "labels": {"reads": [{"table": "economic_diversification", "columns": null}], "writes": [{"table": "platformh", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO store_district SELECT a.account_type, b.area_name FROM co2emissions a JOIN service_budget b ON a.elevation = b.elevation\"\n", "labels": {"reads": [{"table": "co2emissions", "columns": null}, {"table": "service_budget", "columns": null}], "writes": [{"table": "store_district", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mobile_usage\"\n", "labels": {"reads": [{"table": "mobile_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_cart_item\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws_cart_item", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ods.ods_coupon_use_di\"\n", "labels": {"reads": [{"table": "ods.ods_coupon_use_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT num_owners, lastclaimdate FROM climate_finance_asia LIMIT 343\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO dwd.dwd_payments_full SELECT premise_id, city_name FROM dams WHERE premise_id > 27\")\n", "labels": {"reads": [{"table": "climate_finance_asia", "columns": ["num_owners", "lastclaimdate"]}, {"table": "dams", "columns": ["premise_id", "city_name"]}], "writes": [{"table": "dwd.dwd_payments_full", "columns": ["premise_id", "city_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_source(ctx, \"organic_cosmetics\")\ndump_to_sink(df, \"acceptance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": null}], "writes": [{"table": "acceptance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"singer_in_concert\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.refunds_hourly\")\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": [{"table": "stg.refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO user_stats (market_rate, artifact_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "user_stats", "columns": ["market_rate", "artifact_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fare_segments\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"student\")\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": [{"table": "student", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO station_company SELECT coupon_id, updatedate FROM readership WHERE coupon_id > 267\")\n", "labels": {"reads": [{"table": "readership", "columns": ["coupon_id", "updatedate"]}], "writes": [{"table": "station_company", "columns": ["coupon_id", "updatedate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"donationhistory\").where(\"dt = current_date()\").writeTo(\"ods_shipments_df\").append()\n", "labels": {"reads": [{"table": "donationhistory", "columns": null}], "writes": [{"table": "ods_shipments_df", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO train_maintenance SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT software_platform, category FROM hotel_business_partnerships\", engine)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"organicproducts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hotel_business_partnerships", "columns": ["software_platform", "category"]}], "writes": [{"table": "organicproducts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"diversification_projects\")\npush_to_warehouse(df, \"military_spending\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "diversification_projects", "columns": null}], "writes": [{"table": "military_spending", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO fault_log_parts SELECT * FROM legacy\nspark.sql(\"INSERT INTO historicalcontexts SELECT visit_date, starting_year, topic FROM device_accessibility WHERE visit_date > 60\")\n", "labels": {"reads": [{"table": "device_accessibility", "columns": ["visit_date", "starting_year", "topic"]}], "writes": [{"table": "historicalcontexts", "columns": ["visit_date", "starting_year", "topic"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sentence_length, area_sqkm FROM arctictemperature LIMIT 105\")\nrows = cur.fetchall()\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "arctictemperature", "columns": ["sentence_length", "area_sqkm"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO donations2022 SELECT join_year, accreditation_level, tier, venue FROM co2_sequestration WHERE join_year > 29\"\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": ["join_year", "accreditation_level", "tier", "venue"]}], "writes": [{"table": "donations2022", "columns": ["join_year", "accreditation_level", "tier", "venue"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM donation\", conn)\ndf.to_sql(\"staff_department_assignments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "donation", "columns": null}], "writes": [{"table": "staff_department_assignments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 378;\nEOF\n", "labels": {"reads": [{"table": "criticalincidents", "columns": ["aid_id", "vessel"]}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": ["aid_id", "vessel"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"workplaces\")\nsave_to_warehouse(df, \"person\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "workplaces", "columns": null}], "writes": [{"table": "person", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT nation, dockingdate FROM chargingstations LIMIT 478\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "chargingstations", "columns": ["nation", "dockingdate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO indigenouscommunities SELECT date_account_opened, mine_type FROM volunteer_events WHERE date_account_opened > 152\"\n", "labels": {"reads": [{"table": "volunteer_events", "columns": ["date_account_opened", "mine_type"]}], "writes": [{"table": "indigenouscommunities", "columns": ["date_account_opened", "mine_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO exhibition_visits SELECT cargo_weight, publication_date FROM stg.campaigns_df WHERE cargo_weight > 480\"], check=True)\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": ["cargo_weight", "publication_date"]}], "writes": [{"table": "exhibition_visits", "columns": ["cargo_weight", "publication_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO dws.payments_delta SELECT attendeename, pages_per_minute_color FROM lead_mines WHERE attendeename > 268\")\n", "labels": {"reads": [{"table": "lead_mines", "columns": ["attendeename", "pages_per_minute_color"]}], "writes": [{"table": "dws.payments_delta", "columns": ["attendeename", "pages_per_minute_color"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"sales_2\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "sales_2", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"flight_safety\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"emergencies\")\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": [{"table": "emergencies", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"attorneylocationyear\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "attorneylocationyear", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_reactions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"hospitallocations\")\n", "labels": {"reads": [{"table": "user_reactions", "columns": null}], "writes": [{"table": "hospitallocations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM topublictransportation\", conn)\ndf.to_sql(\"menu_engineering\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "topublictransportation", "columns": null}], "writes": [{"table": "menu_engineering", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 166;\nSQL\n", "labels": {"reads": [{"table": "affordablehousing", "columns": ["undergraduate", "contact_number"]}, {"table": "continent", "columns": ["avg_speed", "awayteamid", "session_name", "investor_id"]}], "writes": [{"table": "music_events", "columns": ["avg_speed", "awayteamid", "session_name", "investor_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"third_party_companies\").toPandas()\ndf[[\"operating_system\", \"trial_status\"]].to_sql(\"facility_production\", engine, index=False)\n", "labels": {"reads": [{"table": "third_party_companies", "columns": null}], "writes": [{"table": "facility_production", "columns": ["operating_system", "trial_status"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT mine_location, apt_type_code FROM arcticocean LIMIT 478\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO restaurant SELECT code, area, purchase_date, component_type FROM aquaticfarm WHERE code > 468\")\n", "labels": {"reads": [{"table": "arcticocean", "columns": ["mine_location", "apt_type_code"]}, {"table": "aquaticfarm", "columns": ["code", "area", "purchase_date", "component_type"]}], "writes": [{"table": "restaurant", "columns": ["code", "area", "purchase_date", "component_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table teams --columns passengers,treatment --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "teams", "columns": ["passengers", "treatment"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date, issue_count FROM rebounds LIMIT 422\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "rebounds", "columns": ["date", "issue_count"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model marine_life_populations depends on community_centers\ndbt build --models marine_life_populations --vars 'source: community_centers'\n", "labels": {"reads": [{"table": "community_centers", "columns": null}], "writes": [{"table": "marine_life_populations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO rural_feeder_roads SELECT date_of_notes, trip_id, gas_production_2020, round FROM labor_hours WHERE date_of_notes > 476\")\n", "labels": {"reads": [{"table": "labor_hours", "columns": ["date_of_notes", "trip_id", "gas_production_2020", "round"]}], "writes": [{"table": "rural_feeder_roads", "columns": ["date_of_notes", "trip_id", "gas_production_2020", "round"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT membergender, lot_id FROM basketball_teams\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"temperaturehistory\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "basketball_teams", "columns": ["membergender", "lot_id"]}], "writes": [{"table": "temperaturehistory", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 472;\nEOF\n", "labels": {"reads": [{"table": "hotel_chains", "columns": ["semester", "decor", "form_type_code"]}], "writes": [{"table": "sustainable_urban", "columns": ["semester", "decor", "form_type_code"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT kids, source_u_id FROM ads.ads_users_hourly LIMIT 53\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": ["kids", "source_u_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM solana_transactions\", conn)\ndf.to_sql(\"nba_games\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "solana_transactions", "columns": null}], "writes": [{"table": "nba_games", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods.ods_member_point_df SELECT a.provider_name, b.building_id FROM workshop a JOIN community_events b ON a.decision = b.decision\"\n", "labels": {"reads": [{"table": "workshop", "columns": null}, {"table": "community_events", "columns": null}], "writes": [{"table": "ods.ods_member_point_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bike_station_info\").where(\"dt = current_date()\").writeTo(\"shops\").append()\n", "labels": {"reads": [{"table": "bike_station_info", "columns": null}], "writes": [{"table": "shops", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO attribute_definitions SELECT document_date, model_name, operation, vehicle_id FROM customerorders WHERE document_date > 427\"\n", "labels": {"reads": [{"table": "customerorders", "columns": ["document_date", "model_name", "operation", "vehicle_id"]}], "writes": [{"table": "attribute_definitions", "columns": ["document_date", "model_name", "operation", "vehicle_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT disability_type, shipment_tracking_number FROM fairtradecertification LIMIT 96\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO paris_train SELECT course_id, follows_ethical_practices, event_type_id FROM dwd.sessions WHERE course_id > 193\")\n", "labels": {"reads": [{"table": "fairtradecertification", "columns": ["disability_type", "shipment_tracking_number"]}, {"table": "dwd.sessions", "columns": ["course_id", "follows_ethical_practices", "event_type_id"]}], "writes": [{"table": "paris_train", "columns": ["course_id", "follows_ethical_practices", "event_type_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO renewableenergyprojects SELECT annual_entry_exit, publication_year, albumname, market_id FROM carbon_offset_programs WHERE annual_entry_exit > 397\"\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": ["annual_entry_exit", "publication_year", "albumname", "market_id"]}], "writes": [{"table": "renewableenergyprojects", "columns": ["annual_entry_exit", "publication_year", "albumname", "market_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO album (supply_volume, sale_amount) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "album", "columns": ["supply_volume", "sale_amount"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi.bi_events_full\");\ndf.write().mode(\"overwrite\").saveAsTable(\"mining_operations\");\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}], "writes": [{"table": "mining_operations", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 387;\nSQL\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": ["spacecraft_id", "incident_type"]}, {"table": "social_impact_bonds", "columns": ["createdate", "provider_name", "emp_num"]}], "writes": [{"table": "birds", "columns": ["createdate", "provider_name", "emp_num"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"firestations\")\nsave_to_output(df, \"evidence_based_policies\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "firestations", "columns": null}], "writes": [{"table": "evidence_based_policies", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sponsor_trials\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"opioid_overdoses\")\n", "labels": {"reads": [{"table": "sponsor_trials", "columns": null}], "writes": [{"table": "opioid_overdoses", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 413;\nEOF\n", "labels": {"reads": [{"table": "calibration_data2", "columns": ["apt_id", "reason", "goldquantity"]}], "writes": [{"table": "bustrips", "columns": ["apt_id", "reason", "goldquantity"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO resource_extraction (studentid, reviews) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "resource_extraction", "columns": ["studentid", "reviews"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"rooms\").where(\"dt = current_date()\").writeTo(\"stg.stg_risk_score\").append()\n", "labels": {"reads": [{"table": "rooms", "columns": null}], "writes": [{"table": "stg.stg_risk_score", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO rural_feeder_roads (initiativeid, materialtype) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "rural_feeder_roads", "columns": ["initiativeid", "materialtype"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM catalog_structure\"\n", "labels": {"reads": [{"table": "catalog_structure", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO canada_cosmetics_preferences (customer_first_name, energy_efficiency_kwh_m2_year) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "canada_cosmetics_preferences", "columns": ["customer_first_name", "energy_efficiency_kwh_m2_year"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi_device_log_daily SELECT * FROM legacy\ncur.execute(\"SELECT project_category, total_cost FROM investmentsesg LIMIT 55\")\n", "labels": {"reads": [{"table": "investmentsesg", "columns": ["project_category", "total_cost"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model film_actor depends on ocean_salinity\ndbt build --models film_actor --vars '{\"src\":\"ocean_salinity\"}'\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": null}], "writes": [{"table": "film_actor", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"enrollments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "enrollments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO agricultural_innovations SELECT hardware_colours, grant_id, winery FROM chargingstations WHERE hardware_colours > 488\"], check=True)\n", "labels": {"reads": [{"table": "chargingstations", "columns": ["hardware_colours", "grant_id", "winery"]}], "writes": [{"table": "agricultural_innovations", "columns": ["hardware_colours", "grant_id", "winery"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT equipment_name, business_size FROM safety_research LIMIT 150\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO bank SELECT emp_num, attendeeid, round FROM dwd.dwd_events_delta WHERE emp_num > 353\")\n", "labels": {"reads": [{"table": "safety_research", "columns": ["equipment_name", "business_size"]}, {"table": "dwd.dwd_events_delta", "columns": ["emp_num", "attendeeid", "round"]}], "writes": [{"table": "bank", "columns": ["emp_num", "attendeeid", "round"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_input(ctx, \"stg.refunds\")\nsink_to_store(df, \"public_transportation_sydney\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.refunds", "columns": null}], "writes": [{"table": "public_transportation_sydney", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"productsafety\");\ndf.write().mode(\"overwrite\").saveAsTable(\"vessel_incident_count\");\n", "labels": {"reads": [{"table": "productsafety", "columns": null}], "writes": [{"table": "vessel_incident_count", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table military_personnel --target-dir /tmp/land\n", "labels": {"reads": [{"table": "military_personnel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO aquaculture_farms SELECT claim_date, case_number, gdp, wellbeing_score FROM artists WHERE claim_date > 386\");\n", "labels": {"reads": [{"table": "artists", "columns": ["claim_date", "case_number", "gdp", "wellbeing_score"]}], "writes": [{"table": "aquaculture_farms", "columns": ["claim_date", "case_number", "gdp", "wellbeing_score"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO transaction SELECT fundingagency, center, document_id FROM block WHERE fundingagency > 5\"\n", "labels": {"reads": [{"table": "block", "columns": ["fundingagency", "center", "document_id"]}], "writes": [{"table": "transaction", "columns": ["fundingagency", "center", "document_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO community_leaders SELECT * FROM legacy\ncur.execute(\"SELECT amount_due, journal_id FROM renewable_energy_investments LIMIT 497\")\n", "labels": {"reads": [{"table": "renewable_energy_investments", "columns": ["amount_due", "journal_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"monitoring_zones\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"therapists\")\n", "labels": {"reads": [{"table": "monitoring_zones", "columns": null}], "writes": [{"table": "therapists", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO contract_states (retailer, production) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "contract_states", "columns": ["retailer", "production"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO makeup_products SELECT * FROM legacy\ncur.execute(\"SELECT ota_id, trip_date FROM bikerental LIMIT 373\")\n", "labels": {"reads": [{"table": "bikerental", "columns": ["ota_id", "trip_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO retail_workers_union SELECT * FROM legacy\ncur.execute(\"SELECT council_id, risk_score FROM dws.dws_clicks_full LIMIT 409\")\n", "labels": {"reads": [{"table": "dws.dws_clicks_full", "columns": ["council_id", "risk_score"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.mine_location > 435).all()\n# src table: league_x\nengine.execute(\"INSERT INTO mart.mart_users SELECT * FROM league_x\")\n", "labels": {"reads": [{"table": "league_x", "columns": null}], "writes": [{"table": "mart.mart_users", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO hotel_chains SELECT a.matchdate, b.country_code FROM freshwater_fish_farms a JOIN num_employees b ON a.participatedinesports = b.participatedinesports\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "freshwater_fish_farms", "columns": null}, {"table": "num_employees", "columns": null}], "writes": [{"table": "hotel_chains", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table attorney_billing_rates --target-dir /tmp/land\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"highways\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"nyc_subway\")\n", "labels": {"reads": [{"table": "highways", "columns": null}], "writes": [{"table": "nyc_subway", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart.mart_device_log_delta SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT num_sessions, incident_name FROM highways LIMIT 119\")\nif not rows:\n logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO travel_advisory SELECT build_date, replacement_cost, transaction_amount, building_address FROM waste_generation_metrics WHERE build_date > 170\")\n", "labels": {"reads": [{"table": "highways", "columns": ["num_sessions", "incident_name"]}, {"table": "waste_generation_metrics", "columns": ["build_date", "replacement_cost", "transaction_amount", "building_address"]}], "writes": [{"table": "travel_advisory", "columns": ["build_date", "replacement_cost", "transaction_amount", "building_address"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table vesselfuel --columns unit,money_requested --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "vesselfuel", "columns": ["unit", "money_requested"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO australia_offset_programs SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 142;\nEOF\n", "labels": {"reads": [{"table": "program", "columns": ["state_province", "union_member"]}], "writes": [{"table": "militaryequipment", "columns": ["state_province", "union_member"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 326;\nEOF\n", "labels": {"reads": [{"table": "ai_projects", "columns": ["province_id", "carriername"]}], "writes": [{"table": "ods.ods_member_point_df", "columns": ["province_id", "carriername"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 38;\nSQL\n", "labels": {"reads": [{"table": "product_info", "columns": ["sensor_type", "mission_date"]}, {"table": "paintings", "columns": ["extraction_date", "case_burden", "contract_address", "refugee_id"]}], "writes": [{"table": "freshwaterfinfish", "columns": ["extraction_date", "case_burden", "contract_address", "refugee_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"royal_family\")\nsink_to_target(df, \"workout_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "royal_family", "columns": null}], "writes": [{"table": "workout_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO safetytests (hospital_id, fish_count) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "safetytests", "columns": ["hospital_id", "fish_count"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"files\").toPandas()\ndf[[\"mission_date\", \"resident_id\"]].to_sql(\"diversion_programs\", engine, index=False)\n", "labels": {"reads": [{"table": "files", "columns": null}], "writes": [{"table": "diversion_programs", "columns": ["mission_date", "resident_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vendorfabrics SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_shipments\").toPandas()\ndf[[\"awards\", \"contact_staff_id\"]].to_sql(\"dorm_amenity\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.bi_shipments", "columns": null}], "writes": [{"table": "dorm_amenity", "columns": ["awards", "contact_staff_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi_orders_daily SELECT tourist_id, author_community FROM sustainable_tourism_practices WHERE tourist_id > 243\"\n", "labels": {"reads": [{"table": "sustainable_tourism_practices", "columns": ["tourist_id", "author_community"]}], "writes": [{"table": "bi_orders_daily", "columns": ["tourist_id", "author_community"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.sessions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads_coupon_use_full\")\n", "labels": {"reads": [{"table": "ods.sessions", "columns": null}], "writes": [{"table": "ads_coupon_use_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO opioid_overdoses SELECT minister, review_id FROM virtual_tourism WHERE minister > 486\")\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": ["minister", "review_id"]}], "writes": [{"table": "opioid_overdoses", "columns": ["minister", "review_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO market SELECT partner_id, itemname FROM trust WHERE partner_id > 455\"\n", "labels": {"reads": [{"table": "trust", "columns": ["partner_id", "itemname"]}], "writes": [{"table": "market", "columns": ["partner_id", "itemname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO trenches SELECT * FROM legacy\ncur.execute(\"SELECT spacecraft_id, number_of_platforms FROM ads.orders_daily LIMIT 480\")\n", "labels": {"reads": [{"table": "ads.orders_daily", "columns": ["spacecraft_id", "number_of_platforms"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"marine_species_indian\")\nsrc.write.insertInto(\"autonomousdriving\", overwrite=True)\n", "labels": {"reads": [{"table": "marine_species_indian", "columns": null}], "writes": [{"table": "autonomousdriving", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO customer_events SELECT a.treatment_year, b.ngo_name FROM list a JOIN habitat3 b ON a.stageposition = b.stageposition\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "list", "columns": null}, {"table": "habitat3", "columns": null}], "writes": [{"table": "customer_events", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM excavations\", conn)\ndf.to_sql(\"high_risk\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "excavations", "columns": null}], "writes": [{"table": "high_risk", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO ods.ods_events_daily SELECT certified, vr_platform FROM community_events WHERE certified > 173\")\n", "labels": {"reads": [{"table": "community_events", "columns": ["certified", "vr_platform"]}], "writes": [{"table": "ods.ods_events_daily", "columns": ["certified", "vr_platform"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 493;\nSQL\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": ["feedback_id", "consultations"]}, {"table": "ods.ods_events_daily", "columns": ["vulnerability_score", "revenueid", "building_address", "number_of_observations"]}], "writes": [{"table": "global_tournament", "columns": ["vulnerability_score", "revenueid", "building_address", "number_of_observations"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"vessel_safety\")\nsrc.write.insertInto(\"employeedata\", overwrite=True)\n", "labels": {"reads": [{"table": "vessel_safety", "columns": null}], "writes": [{"table": "employeedata", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"donationhistory\")\nsrc.write.insertInto(\"farms\", overwrite=True)\n", "labels": {"reads": [{"table": "donationhistory", "columns": null}], "writes": [{"table": "farms", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"attorney_billing_rates\").where(\"dt = current_date()\").writeTo(\"researchgrants\").append()\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": null}], "writes": [{"table": "researchgrants", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO mart.mart_shipments_hourly SELECT a.record_id, b.gender FROM project_timeline a JOIN ads b ON a.num_students = b.num_students\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "project_timeline", "columns": null}, {"table": "ads", "columns": null}], "writes": [{"table": "mart.mart_shipments_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"manufacturersustainability\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"public_participation\")\n", "labels": {"reads": [{"table": "manufacturersustainability", "columns": null}], "writes": [{"table": "public_participation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO dw.exposure_di SELECT a.receipt_date, b.date_joined_staff FROM drought_impact a JOIN projectemployees b ON a.session_name = b.session_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "drought_impact", "columns": null}, {"table": "projectemployees", "columns": null}], "writes": [{"table": "dw.exposure_di", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO stock_levels SELECT chemical, visit_details FROM part_faults WHERE chemical > 178\")\n", "labels": {"reads": [{"table": "part_faults", "columns": ["chemical", "visit_details"]}], "writes": [{"table": "stock_levels", "columns": ["chemical", "visit_details"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO customer_address_history SELECT attraction_type_description, surname FROM dw.dw_inventory_delta WHERE attraction_type_description > 323\"\n", "labels": {"reads": [{"table": "dw.dw_inventory_delta", "columns": ["attraction_type_description", "surname"]}], "writes": [{"table": "customer_address_history", "columns": ["attraction_type_description", "surname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table invoices --columns materialtype,donation_amount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "invoices", "columns": ["materialtype", "donation_amount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO movies SELECT donator_name, case_id, low_income_neighborhood, member FROM institution WHERE donator_name > 59\")\n", "labels": {"reads": [{"table": "institution", "columns": ["donator_name", "case_id", "low_income_neighborhood", "member"]}], "writes": [{"table": "movies", "columns": ["donator_name", "case_id", "low_income_neighborhood", "member"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemicalproducts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bi.products_daily\")\n", "labels": {"reads": [{"table": "chemicalproducts", "columns": null}], "writes": [{"table": "bi.products_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"player_sessions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "player_sessions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO student_tests_taken SELECT traveler_id, response_time, hardware_model_name, assets FROM members WHERE traveler_id > 96\")\n", "labels": {"reads": [{"table": "members", "columns": ["traveler_id", "response_time", "hardware_model_name", "assets"]}], "writes": [{"table": "student_tests_taken", "columns": ["traveler_id", "response_time", "hardware_model_name", "assets"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT playerid, transactions FROM premises LIMIT 158\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "premises", "columns": ["playerid", "transactions"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO city_waste_generation SELECT contactid, donorname, count_date FROM reo_production WHERE contactid > 394\")\n", "labels": {"reads": [{"table": "reo_production", "columns": ["contactid", "donorname", "count_date"]}], "writes": [{"table": "city_waste_generation", "columns": ["contactid", "donorname", "count_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"gamesessions\");\ndf.write().mode(\"overwrite\").saveAsTable(\"marine_life_data\");\n", "labels": {"reads": [{"table": "gamesessions", "columns": null}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads_refunds_full SELECT * FROM legacy\ncur.execute(\"SELECT category, high_temperature FROM stg.stg_exposure_di LIMIT 292\")\n", "labels": {"reads": [{"table": "stg.stg_exposure_di", "columns": ["category", "high_temperature"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"accessible_tech_categories\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"chemical\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "chemical", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM permit\", conn)\ndf.to_sql(\"appointment\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "permit", "columns": null}], "writes": [{"table": "appointment", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO school_districts SELECT * FROM legacy\ncur.execute(\"SELECT market_share, spacecraft_name FROM departments LIMIT 189\")\n", "labels": {"reads": [{"table": "departments", "columns": ["market_share", "spacecraft_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"electoral_register\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"waste_generation_metrics\")\n", "labels": {"reads": [{"table": "electoral_register", "columns": null}], "writes": [{"table": "waste_generation_metrics", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM minor_in\"\n", "labels": {"reads": [{"table": "minor_in", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model users_daily depends on jupiter_missions\ndbt build --select users_daily --vars '{\"src\":\"jupiter_missions\"}'\n", "labels": {"reads": [{"table": "jupiter_missions", "columns": null}], "writes": [{"table": "users_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO tickets SELECT * FROM legacy\ncur.execute(\"SELECT call_time, crop_name FROM bi.member_point_full LIMIT 426\")\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": ["call_time", "crop_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT museum_name, grantid FROM clinics_sa\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"student_mental_health\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "clinics_sa", "columns": ["museum_name", "grantid"]}], "writes": [{"table": "student_mental_health", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO marine_life_research SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"mart_exposure_hourly\")\npush_to_output(df, \"dw.dw_inventory_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart_exposure_hourly", "columns": null}], "writes": [{"table": "dw.dw_inventory_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO peacekeeping_units SELECT * FROM legacy\ncur.execute(\"SELECT material, award FROM sports_events LIMIT 439\")\n", "labels": {"reads": [{"table": "sports_events", "columns": ["material", "award"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO model_fairness SELECT forest_id, last_workout_date FROM fleet WHERE forest_id > 10\");\n", "labels": {"reads": [{"table": "fleet", "columns": ["forest_id", "last_workout_date"]}], "writes": [{"table": "model_fairness", "columns": ["forest_id", "last_workout_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO seamounts SELECT vessel_id, donor FROM trends_2022 WHERE vessel_id > 332\")\n", "labels": {"reads": [{"table": "trends_2022", "columns": ["vessel_id", "donor"]}], "writes": [{"table": "seamounts", "columns": ["vessel_id", "donor"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dockingdate, time_stamp FROM dw.dw_sessions_full LIMIT 362\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "dw.dw_sessions_full", "columns": ["dockingdate", "time_stamp"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO gameattendance (dorm_name, blockcode) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "gameattendance", "columns": ["dorm_name", "blockcode"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO host SELECT song_name, violationid FROM militaryequipment WHERE song_name > 383\")\n", "labels": {"reads": [{"table": "militaryequipment", "columns": ["song_name", "violationid"]}], "writes": [{"table": "host", "columns": ["song_name", "violationid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"manager_award\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "manager_award", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM community.donors\"\n", "labels": {"reads": [{"table": "community.donors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO equipmentsales SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT quantity_containers, therapeutic_area FROM satellite_missions_large\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"associatedheritages\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": ["quantity_containers", "therapeutic_area"]}], "writes": [{"table": "associatedheritages", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO seafood (major, first_year) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "seafood", "columns": ["major", "first_year"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO street_markets (education_id, resource_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "street_markets", "columns": ["education_id", "resource_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO commercialbuildings SELECT yield_id, release_year FROM creativeais WHERE yield_id > 87\"\n", "labels": {"reads": [{"table": "creativeais", "columns": ["yield_id", "release_year"]}], "writes": [{"table": "commercialbuildings", "columns": ["yield_id", "release_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table community_development.transactions --columns end_date,credits --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "community_development.transactions", "columns": ["end_date", "credits"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT menuname, mental_health_resource_access FROM gamesales\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"bi.bi_vendors_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "gamesales", "columns": ["menuname", "mental_health_resource_access"]}], "writes": [{"table": "bi.bi_vendors_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model station_crime_rates depends on areas\ndbt run --select station_crime_rates --vars '{\"source_table\":\"areas\"}'\n", "labels": {"reads": [{"table": "areas", "columns": null}], "writes": [{"table": "station_crime_rates", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table defense_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "defense_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT unit_of_measure, therapeutic_area FROM microfinance_clients LIMIT 273\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "microfinance_clients", "columns": ["unit_of_measure", "therapeutic_area"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO bioprocesses SELECT a.founder_identifies_as_lgbtq, b.drugname FROM bookings a JOIN landfills b ON a.ai_id = b.ai_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bookings", "columns": null}, {"table": "landfills", "columns": null}], "writes": [{"table": "bioprocesses", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dw.dw_orders_hourly SELECT title, production_cost FROM wildlife_sanctuaries WHERE title > 350\"\n", "labels": {"reads": [{"table": "wildlife_sanctuaries", "columns": ["title", "production_cost"]}], "writes": [{"table": "dw.dw_orders_hourly", "columns": ["title", "production_cost"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO impact_asia SELECT restorative_justice, operation, success, wildlife_type_id FROM sustainability_metrics WHERE restorative_justice > 244\");\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": ["restorative_justice", "operation", "success", "wildlife_type_id"]}], "writes": [{"table": "impact_asia", "columns": ["restorative_justice", "operation", "success", "wildlife_type_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"retail_workers_union\")\npush_to_store(df, \"tracklists\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "retail_workers_union", "columns": null}], "writes": [{"table": "tracklists", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.monthlyactiveusers > 438).all()\n# src table: projectemployees\nengine.execute(\"INSERT INTO financial_capability_programs SELECT * FROM projectemployees\")\n", "labels": {"reads": [{"table": "projectemployees", "columns": null}], "writes": [{"table": "financial_capability_programs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO threat_intelligence SELECT party, trial_id FROM well_production WHERE party > 119\");\n", "labels": {"reads": [{"table": "well_production", "columns": ["party", "trial_id"]}], "writes": [{"table": "threat_intelligence", "columns": ["party", "trial_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model tokyo_water_consumption depends on assignedto\ndbt build --models tokyo_water_consumption --vars 'source: assignedto'\n", "labels": {"reads": [{"table": "assignedto", "columns": null}], "writes": [{"table": "tokyo_water_consumption", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"user\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "user", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"culturalcompetencytrainings\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT reservoir_id, zone_id FROM ads.ads_products_hourly LIMIT 118\")\nrows = cur.fetchall()\nimport logging\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "ads.ads_products_hourly", "columns": ["reservoir_id", "zone_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"seasonalvegetables\").toPandas()\ndf[[\"funding_year\", \"actual_order_id\"]].to_sql(\"catalog_contents\", engine, index=False)\n", "labels": {"reads": [{"table": "seasonalvegetables", "columns": null}], "writes": [{"table": "catalog_contents", "columns": ["funding_year", "actual_order_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd.dwd_payments (destroyed_by_employee_id, longitude) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_payments", "columns": ["destroyed_by_employee_id", "longitude"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessel_incident_count\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"medicine_enzyme_interaction\")\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": null}], "writes": [{"table": "medicine_enzyme_interaction", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"weather_record\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "weather_record", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"parity_violations\")\npush_to_warehouse(df, \"transport\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "parity_violations", "columns": null}], "writes": [{"table": "transport", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fabrics SELECT a.union_member_id, b.account_details FROM workforce_training a JOIN benefits_overpayments b ON a.start = b.start\"\n", "labels": {"reads": [{"table": "workforce_training", "columns": null}, {"table": "benefits_overpayments", "columns": null}], "writes": [{"table": "fabrics", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO excavation SELECT kids, editor_id, attendance_id FROM mart.mart_users WHERE kids > 444\"\n", "labels": {"reads": [{"table": "mart.mart_users", "columns": ["kids", "editor_id", "attendance_id"]}], "writes": [{"table": "excavation", "columns": ["kids", "editor_id", "attendance_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO african_tourism SELECT * FROM legacy\ncur.execute(\"SELECT asset_model, editor_id FROM animals LIMIT 276\")\n", "labels": {"reads": [{"table": "animals", "columns": ["asset_model", "editor_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT albumname, is_false FROM vulnerabilities LIMIT 235\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "vulnerabilities", "columns": ["albumname", "is_false"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO makeup_sales SELECT building_manager, transact_date, organization_details FROM shrimp_farms WHERE building_manager > 499\")\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": ["building_manager", "transact_date", "organization_details"]}], "writes": [{"table": "makeup_sales", "columns": ["building_manager", "transact_date", "organization_details"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_inventory SELECT a.subject_area_id, b.doctor_id FROM dw.dw_coupon_use_daily a JOIN timbersales b ON a.archaeologistid = b.archaeologistid\"\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": null}, {"table": "timbersales", "columns": null}], "writes": [{"table": "bi.bi_inventory", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_campaigns_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi_campaigns_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"store\").toPandas()\ndf[[\"suppliername\", \"date_payment_made\"]].to_sql(\"workerbuildings\", engine, index=False)\n", "labels": {"reads": [{"table": "store", "columns": null}], "writes": [{"table": "workerbuildings", "columns": ["suppliername", "date_payment_made"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"properties\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stg.stg_campaigns\")\n", "labels": {"reads": [{"table": "properties", "columns": null}], "writes": [{"table": "stg.stg_campaigns", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ytterbium_supply\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws.device_log_df\")\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": null}], "writes": [{"table": "dws.device_log_df", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 287;\nEOF\n", "labels": {"reads": [{"table": "movie_financials", "columns": ["fair_labor", "active_from_date", "shipmentid", "assessment_score"]}], "writes": [{"table": "ancient_cultures", "columns": ["fair_labor", "active_from_date", "shipmentid", "assessment_score"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO incident SELECT a.customer, b.min_depth FROM lead_mines a JOIN accessibility_audits b ON a.menuitem = b.menuitem\"\n", "labels": {"reads": [{"table": "lead_mines", "columns": null}, {"table": "accessibility_audits", "columns": null}], "writes": [{"table": "incident", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO military_sales SELECT * FROM legacy\nspark.sql(\"INSERT INTO gender SELECT deliverydate, participation_id, staystart, state_province_county FROM carbon_offset_south_america WHERE deliverydate > 351\")\n", "labels": {"reads": [{"table": "carbon_offset_south_america", "columns": ["deliverydate", "participation_id", "staystart", "state_province_county"]}], "writes": [{"table": "gender", "columns": ["deliverydate", "participation_id", "staystart", "state_province_county"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO space_missions (promotiondate, fertilizer_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "space_missions", "columns": ["promotiondate", "fertilizer_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"strandings\").where(\"dt = current_date()\").writeTo(\"fleet\").append()\n", "labels": {"reads": [{"table": "strandings", "columns": null}], "writes": [{"table": "fleet", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO materials SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO areas (energy_efficiency_rating, mine_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "areas", "columns": ["energy_efficiency_rating", "mine_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO posts_per_day SELECT bioprocess_id, eid, authorder FROM bi.inventory_daily WHERE bioprocess_id > 260\"\n", "labels": {"reads": [{"table": "bi.inventory_daily", "columns": ["bioprocess_id", "eid", "authorder"]}], "writes": [{"table": "posts_per_day", "columns": ["bioprocess_id", "eid", "authorder"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO mailshot_campaigns SELECT * FROM legacy\nspark.sql(\"INSERT INTO energy_efficiency_projects SELECT signup_date, offense, round, annual_carbon_offsets FROM indigenous_communities WHERE signup_date > 366\")\n", "labels": {"reads": [{"table": "indigenous_communities", "columns": ["signup_date", "offense", "round", "annual_carbon_offsets"]}], "writes": [{"table": "energy_efficiency_projects", "columns": ["signup_date", "offense", "round", "annual_carbon_offsets"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hashtags, health_equity_metric_1 FROM atlantic_plate\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"labour_productivity\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "atlantic_plate", "columns": ["hashtags", "health_equity_metric_1"]}], "writes": [{"table": "labour_productivity", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO foodaid SELECT crop, last_name, sustainablepractices, research_id FROM culturalpractices WHERE crop > 51\")\n", "labels": {"reads": [{"table": "culturalpractices", "columns": ["crop", "last_name", "sustainablepractices", "research_id"]}], "writes": [{"table": "foodaid", "columns": ["crop", "last_name", "sustainablepractices", "research_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO spacecraftmanufacturing SELECT * FROM legacy\ncur.execute(\"SELECT ota_id, salary FROM ma_inspections LIMIT 426\")\n", "labels": {"reads": [{"table": "ma_inspections", "columns": ["ota_id", "salary"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT facility_code, state_county FROM marketing_budgets LIMIT 203\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "marketing_budgets", "columns": ["facility_code", "state_county"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"forest_species\").toPandas()\ndf[[\"project_id\", \"participant\"]].to_sql(\"resource_extraction\", engine, index=False)\n", "labels": {"reads": [{"table": "forest_species", "columns": null}], "writes": [{"table": "resource_extraction", "columns": ["project_id", "participant"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT feedtype, sales FROM student_courses LIMIT 173\")\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO criticalincidents SELECT hardware_colours, permits_issued, maintenance_id FROM water_consumption WHERE hardware_colours > 16\")\n", "labels": {"reads": [{"table": "student_courses", "columns": ["feedtype", "sales"]}, {"table": "water_consumption", "columns": ["hardware_colours", "permits_issued", "maintenance_id"]}], "writes": [{"table": "criticalincidents", "columns": ["hardware_colours", "permits_issued", "maintenance_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO forests SELECT booking_status_code, merchandise_id, donation_amount FROM sales WHERE booking_status_code > 265\"], check=True)\n", "labels": {"reads": [{"table": "sales", "columns": ["booking_status_code", "merchandise_id", "donation_amount"]}], "writes": [{"table": "forests", "columns": ["booking_status_code", "merchandise_id", "donation_amount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table performances --target-dir /tmp/land\n", "labels": {"reads": [{"table": "performances", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ref_locations SELECT trainingid, num_hotels, casetype, employee_address_id FROM food_justice_contributors WHERE trainingid > 304\"\n", "labels": {"reads": [{"table": "food_justice_contributors", "columns": ["trainingid", "num_hotels", "casetype", "employee_address_id"]}], "writes": [{"table": "ref_locations", "columns": ["trainingid", "num_hotels", "casetype", "employee_address_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO cmi_cross_references SELECT a.location, b.framework_name FROM artists a JOIN purchases b ON a.tree_species = b.tree_species\"\n", "labels": {"reads": [{"table": "artists", "columns": null}, {"table": "purchases", "columns": null}], "writes": [{"table": "cmi_cross_references", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"clothingitems\").where(\"dt = current_date()\").writeTo(\"gymnast\").append()\n", "labels": {"reads": [{"table": "clothingitems", "columns": null}], "writes": [{"table": "gymnast", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO department_publications SELECT * FROM legacy\nspark.sql(\"INSERT INTO clinical_trials SELECT owner, training_name FROM trends_2022 WHERE owner > 79\")\n", "labels": {"reads": [{"table": "trends_2022", "columns": ["owner", "training_name"]}], "writes": [{"table": "clinical_trials", "columns": ["owner", "training_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO intelligence_personnel SELECT author_or_editor, ycard, complaint_id FROM initiative_types WHERE author_or_editor > 292\");\n", "labels": {"reads": [{"table": "initiative_types", "columns": ["author_or_editor", "ycard", "complaint_id"]}], "writes": [{"table": "intelligence_personnel", "columns": ["author_or_editor", "ycard", "complaint_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO creative_ai SELECT * FROM legacy\nspark.sql(\"INSERT INTO contractnegotiations SELECT galleryname, year_join, cloud_cover, fare_date FROM water_sources WHERE galleryname > 406\")\n", "labels": {"reads": [{"table": "water_sources", "columns": ["galleryname", "year_join", "cloud_cover", "fare_date"]}], "writes": [{"table": "contractnegotiations", "columns": ["galleryname", "year_join", "cloud_cover", "fare_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"machinery\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"songs\")\n", "labels": {"reads": [{"table": "machinery", "columns": null}], "writes": [{"table": "songs", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"home_game\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"co_ownership\")\n", "labels": {"reads": [{"table": "home_game", "columns": null}], "writes": [{"table": "co_ownership", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT horizontal_bar_points, artifactname FROM materials_usage\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nimport logging\ndf.to_sql(\"patient_outcomes\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "materials_usage", "columns": ["horizontal_bar_points", "artifactname"]}], "writes": [{"table": "patient_outcomes", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM certificate\"\n", "labels": {"reads": [{"table": "certificate", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO philadelphia_police_emergencies SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mammals SELECT store_name, furniture_id, shipment_year, dose FROM fish_purchases WHERE store_name > 202\"\n", "labels": {"reads": [{"table": "fish_purchases", "columns": ["store_name", "furniture_id", "shipment_year", "dose"]}], "writes": [{"table": "mammals", "columns": ["store_name", "furniture_id", "shipment_year", "dose"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO carbon_emissions SELECT hoursspent, game_name, projectid FROM fleets WHERE hoursspent > 441\"\n", "labels": {"reads": [{"table": "fleets", "columns": ["hoursspent", "game_name", "projectid"]}], "writes": [{"table": "carbon_emissions", "columns": ["hoursspent", "game_name", "projectid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"solar_plants\");\ndf.write().mode(\"overwrite\").saveAsTable(\"trainmaintenance\");\n", "labels": {"reads": [{"table": "solar_plants", "columns": null}], "writes": [{"table": "trainmaintenance", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ranking > 15).all()\n# src table: menu_engineering\nengine.execute(\"INSERT INTO customers_cards SELECT * FROM menu_engineering\")\n", "labels": {"reads": [{"table": "menu_engineering", "columns": null}], "writes": [{"table": "customers_cards", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"stops\")\npush_to_sink(df, \"fleet_management\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stops", "columns": null}], "writes": [{"table": "fleet_management", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO visitor_exhibition SELECT numpieces, candidate_id FROM container_receipts WHERE numpieces > 4\"\n", "labels": {"reads": [{"table": "container_receipts", "columns": ["numpieces", "candidate_id"]}], "writes": [{"table": "visitor_exhibition", "columns": ["numpieces", "candidate_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO street_markets SELECT trial_id, customer_id, artist_gender FROM us_cities WHERE trial_id > 244\");\n", "labels": {"reads": [{"table": "us_cities", "columns": ["trial_id", "customer_id", "artist_gender"]}], "writes": [{"table": "street_markets", "columns": ["trial_id", "customer_id", "artist_gender"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table news_views --columns mean_sea_level_pressure_inches,shelter_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "news_views", "columns": ["mean_sea_level_pressure_inches", "shelter_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dwd.inventory_df\"\n", "labels": {"reads": [{"table": "dwd.inventory_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO auto_shows SELECT feedtype, unit FROM cybersecuritybudget WHERE feedtype > 63\")\n", "labels": {"reads": [{"table": "cybersecuritybudget", "columns": ["feedtype", "unit"]}], "writes": [{"table": "auto_shows", "columns": ["feedtype", "unit"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO sportsinfo SELECT * FROM legacy\nspark.sql(\"INSERT INTO stg.stg_inventory_full SELECT wellbeing_score, manufacturername FROM staff_department_assignments WHERE wellbeing_score > 487\")\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["wellbeing_score", "manufacturername"]}], "writes": [{"table": "stg.stg_inventory_full", "columns": ["wellbeing_score", "manufacturername"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO crimes (vehicle_type, asset_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "crimes", "columns": ["vehicle_type", "asset_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO farms (socialimpactscore, s_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "farms", "columns": ["socialimpactscore", "s_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"broadband_customers_global\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table platform --target-dir /tmp/land\n", "labels": {"reads": [{"table": "platform", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO gameattendance SELECT fastestlapspeed, retailer FROM threat_intel WHERE fastestlapspeed > 28\"\n", "labels": {"reads": [{"table": "threat_intel", "columns": ["fastestlapspeed", "retailer"]}], "writes": [{"table": "gameattendance", "columns": ["fastestlapspeed", "retailer"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table candidate_assessments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 298;\nEOF\n", "labels": {"reads": [{"table": "packages", "columns": ["chemical_id", "water_usage", "crime_rate", "enr"]}], "writes": [{"table": "food_justice_contributors", "columns": ["chemical_id", "water_usage", "crime_rate", "enr"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO vrgames SELECT a.num_beds, b.case_status FROM request a JOIN student_program_mapping b ON a.claim_outcome_code = b.claim_outcome_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "request", "columns": null}, {"table": "student_program_mapping", "columns": null}], "writes": [{"table": "vrgames", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO genetics.projects SELECT budgeted, wage FROM classroom WHERE budgeted > 213\");\n", "labels": {"reads": [{"table": "classroom", "columns": ["budgeted", "wage"]}], "writes": [{"table": "genetics.projects", "columns": ["budgeted", "wage"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO waste_generation_city_v2 SELECT * FROM legacy\nspark.sql(\"INSERT INTO baseball_teams SELECT artifactname, studio_name, consumer_id FROM areas WHERE artifactname > 381\")\n", "labels": {"reads": [{"table": "areas", "columns": ["artifactname", "studio_name", "consumer_id"]}], "writes": [{"table": "baseball_teams", "columns": ["artifactname", "studio_name", "consumer_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model playergamehistory depends on restaurant\ndbt run -s playergamehistory --vars '{\"src\":\"restaurant\"}'\n", "labels": {"reads": [{"table": "restaurant", "columns": null}], "writes": [{"table": "playergamehistory", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM individual\", conn)\ndf.to_sql(\"customer_transactions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "individual", "columns": null}], "writes": [{"table": "customer_transactions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.users_full\").toPandas()\ndf[[\"hireyear\", \"satellite_name\"]].to_sql(\"mart.vendors_full\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.users_full", "columns": null}], "writes": [{"table": "mart.vendors_full", "columns": ["hireyear", "satellite_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO field_rainfall SELECT class_section, recycled FROM digital_trends WHERE class_section > 369\")\n", "labels": {"reads": [{"table": "digital_trends", "columns": ["class_section", "recycled"]}], "writes": [{"table": "field_rainfall", "columns": ["class_section", "recycled"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"socialimpactinvestments\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ref_service_types\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"donorprograms\")\n", "labels": {"reads": [{"table": "ref_service_types", "columns": null}], "writes": [{"table": "donorprograms", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.bi_risk_score_df SELECT researcher, hours_played FROM seafoodsouthafricakenya WHERE researcher > 201\"], check=True)\n", "labels": {"reads": [{"table": "seafoodsouthafricakenya", "columns": ["researcher", "hours_played"]}], "writes": [{"table": "bi.bi_risk_score_df", "columns": ["researcher", "hours_played"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM rural_clinics\", conn)\ndf.to_sql(\"perpetrator\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "rural_clinics", "columns": null}], "writes": [{"table": "perpetrator", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fish_feed_factories\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"event\")\n", "labels": {"reads": [{"table": "fish_feed_factories", "columns": null}], "writes": [{"table": "event", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table residents_services --target-dir /tmp/land\n", "labels": {"reads": [{"table": "residents_services", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO mart.mart_users_di SELECT runtime, product_id, experience_id, regionid FROM ai_ethics WHERE runtime > 56\")\n", "labels": {"reads": [{"table": "ai_ethics", "columns": ["runtime", "product_id", "experience_id", "regionid"]}], "writes": [{"table": "mart.mart_users_di", "columns": ["runtime", "product_id", "experience_id", "regionid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO travel_advisory SELECT bank_id, animal, recipient_id, donation_id FROM offender_demographics WHERE bank_id > 133\"], check=True)\n", "labels": {"reads": [{"table": "offender_demographics", "columns": ["bank_id", "animal", "recipient_id", "donation_id"]}], "writes": [{"table": "travel_advisory", "columns": ["bank_id", "animal", "recipient_id", "donation_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO forest SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hotel_chain_name > 69).all()\n# src table: takes\nengine.execute(\"INSERT INTO party_forms SELECT * FROM takes\")\n", "labels": {"reads": [{"table": "takes", "columns": null}], "writes": [{"table": "party_forms", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_warehouses\").toPandas()\ndf[[\"salesperson\", \"contract_start\"]].to_sql(\"indian_ocean_wells\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": null}], "writes": [{"table": "indian_ocean_wells", "columns": ["salesperson", "contract_start"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO dws.payments_delta SELECT a.crime_type, b.grant_amount FROM item_prices a JOIN armed_forces b ON a.invested = b.invested\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "item_prices", "columns": null}, {"table": "armed_forces", "columns": null}], "writes": [{"table": "dws.payments_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO atlantic_plate SELECT songid, phone_number FROM purchase WHERE songid > 139\");\n", "labels": {"reads": [{"table": "purchase", "columns": ["songid", "phone_number"]}], "writes": [{"table": "atlantic_plate", "columns": ["songid", "phone_number"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO biotech.startups SELECT initiative_type, official_native_language, home_team_id FROM genetics.crispr WHERE initiative_type > 105\"\n", "labels": {"reads": [{"table": "genetics.crispr", "columns": ["initiative_type", "official_native_language", "home_team_id"]}], "writes": [{"table": "biotech.startups", "columns": ["initiative_type", "official_native_language", "home_team_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"suppliersfairlabor\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws.cart_item_full\")\n", "labels": {"reads": [{"table": "suppliersfairlabor", "columns": null}], "writes": [{"table": "dws.cart_item_full", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM immunizationrates\", conn)\ndf.to_sql(\"products_in_events\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "immunizationrates", "columns": null}], "writes": [{"table": "products_in_events", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.stg_device_log_daily\"\n", "labels": {"reads": [{"table": "stg.stg_device_log_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO seal_population SELECT ingredient_id, is_autonomous, class_code FROM civilcases WHERE ingredient_id > 128\")\n", "labels": {"reads": [{"table": "civilcases", "columns": ["ingredient_id", "is_autonomous", "class_code"]}], "writes": [{"table": "seal_population", "columns": ["ingredient_id", "is_autonomous", "class_code"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO climatefinance SELECT enrollment_date, enable_location_tracking FROM energy_efficiency_projects WHERE enrollment_date > 316\")\n", "labels": {"reads": [{"table": "energy_efficiency_projects", "columns": ["enrollment_date", "enable_location_tracking"]}], "writes": [{"table": "climatefinance", "columns": ["enrollment_date", "enable_location_tracking"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"accommodations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "accommodations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table patienttreatments --columns maintenanceid,average_age --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "patienttreatments", "columns": ["maintenanceid", "average_age"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT report_type, theftdate FROM diplomacy_events\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"stg_orders_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "diplomacy_events", "columns": ["report_type", "theftdate"]}], "writes": [{"table": "stg_orders_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 37;\nSQL\n", "labels": {"reads": [{"table": "circular_economy", "columns": ["age", "operation"]}, {"table": "ods.vendors_di", "columns": ["sector_id", "coverage_type"]}], "writes": [{"table": "research_staff", "columns": ["sector_id", "coverage_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO taxi_data SELECT supplierid, score FROM cybersecurity_vulnerabilities WHERE supplierid > 240\"\n", "labels": {"reads": [{"table": "cybersecurity_vulnerabilities", "columns": ["supplierid", "score"]}], "writes": [{"table": "taxi_data", "columns": ["supplierid", "score"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table budgets --target-dir /tmp/land\n", "labels": {"reads": [{"table": "budgets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"safety_incident\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"levees\")\n", "labels": {"reads": [{"table": "safety_incident", "columns": null}], "writes": [{"table": "levees", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi_campaigns_delta (menuid, ratingdate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi_campaigns_delta", "columns": ["menuid", "ratingdate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO malicious_activity SELECT shippingmethod, chip_model, checkout, framework_name FROM timber_production WHERE shippingmethod > 240\"\n", "labels": {"reads": [{"table": "timber_production", "columns": ["shippingmethod", "chip_model", "checkout", "framework_name"]}], "writes": [{"table": "malicious_activity", "columns": ["shippingmethod", "chip_model", "checkout", "framework_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT post_id, data_type FROM dws.dws_shipments_full\", engine)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"feedback\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws.dws_shipments_full", "columns": ["post_id", "data_type"]}], "writes": [{"table": "feedback", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO latam_schema.education_budget SELECT * FROM legacy\ncur.execute(\"SELECT number_thousands, paintingid FROM country_renewable_energy LIMIT 472\")\n", "labels": {"reads": [{"table": "country_renewable_energy", "columns": ["number_thousands", "paintingid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"player_coach\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "player_coach", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO astronauts SELECT start_therapy, shipment_year, transact_date, era FROM exit_strategy WHERE start_therapy > 459\")\n", "labels": {"reads": [{"table": "exit_strategy", "columns": ["start_therapy", "shipment_year", "transact_date", "era"]}], "writes": [{"table": "astronauts", "columns": ["start_therapy", "shipment_year", "transact_date", "era"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO training_programs (video_id, clubname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "training_programs", "columns": ["video_id", "clubname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.clicks_hourly --columns violation_count,paintingid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.clicks_hourly", "columns": ["violation_count", "paintingid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO satellites SELECT element_id, views, founder_lgbtq, vaccine_type FROM fabric WHERE element_id > 277\");\n", "labels": {"reads": [{"table": "fabric", "columns": ["element_id", "views", "founder_lgbtq", "vaccine_type"]}], "writes": [{"table": "satellites", "columns": ["element_id", "views", "founder_lgbtq", "vaccine_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"levees\")\nsrc.write.insertInto(\"songs_length\", overwrite=True)\n", "labels": {"reads": [{"table": "levees", "columns": null}], "writes": [{"table": "songs_length", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"astronaut_missions\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"complaints_breakdown\").toPandas()\ndf[[\"characteristic_id\", \"mid\"]].to_sql(\"spending\", engine, index=False)\n", "labels": {"reads": [{"table": "complaints_breakdown", "columns": null}], "writes": [{"table": "spending", "columns": ["characteristic_id", "mid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO pediatricians SELECT a.support_rate, b.race_ethnicity FROM satellites_by_country a JOIN episodes b ON a.recipe_id = b.recipe_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": null}, {"table": "episodes", "columns": null}], "writes": [{"table": "pediatricians", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO digital_trends SELECT seasons, product_stock_number, ocean, ship_name FROM ref_hotel_star_ratings WHERE seasons > 172\")\n", "labels": {"reads": [{"table": "ref_hotel_star_ratings", "columns": ["seasons", "product_stock_number", "ocean", "ship_name"]}], "writes": [{"table": "digital_trends", "columns": ["seasons", "product_stock_number", "ocean", "ship_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO habitat SELECT rating, team_id_loser FROM screen_mode WHERE rating > 340\");\n", "labels": {"reads": [{"table": "screen_mode", "columns": ["rating", "team_id_loser"]}], "writes": [{"table": "habitat", "columns": ["rating", "team_id_loser"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ref_service_types SELECT rural_area, num_of_shops, sent_date, artifact_name FROM strains WHERE rural_area > 244\"\n", "labels": {"reads": [{"table": "strains", "columns": ["rural_area", "num_of_shops", "sent_date", "artifact_name"]}], "writes": [{"table": "ref_service_types", "columns": ["rural_area", "num_of_shops", "sent_date", "artifact_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT show_times_per_day, date_of_birth FROM freshwater_fish_farms LIMIT 208\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "freshwater_fish_farms", "columns": ["show_times_per_day", "date_of_birth"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO plots SELECT area, local_authority, district FROM conservation_projects WHERE area > 110\"\n", "labels": {"reads": [{"table": "conservation_projects", "columns": ["area", "local_authority", "district"]}], "writes": [{"table": "plots", "columns": ["area", "local_authority", "district"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"renewables.renewable_projects\")\nsrc.write.insertInto(\"cargos\", overwrite=True)\n", "labels": {"reads": [{"table": "renewables.renewable_projects", "columns": null}], "writes": [{"table": "cargos", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO community_policing SELECT donationid, route FROM aquaculture_farms WHERE donationid > 478\");\n", "labels": {"reads": [{"table": "aquaculture_farms", "columns": ["donationid", "route"]}], "writes": [{"table": "community_policing", "columns": ["donationid", "route"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table vesselarrivals --target-dir /tmp/land\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM materials_usage\"\n", "labels": {"reads": [{"table": "materials_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table therapy --target-dir /tmp/land\n", "labels": {"reads": [{"table": "therapy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"agro_regions\")\ndump_to_target(df, \"artistsdemographics\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "agro_regions", "columns": null}], "writes": [{"table": "artistsdemographics", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO defense_contractors SELECT a.directed_by, b.chemical_name FROM spaceradar a JOIN pacific_ocean b ON a.sessionid = b.sessionid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "spaceradar", "columns": null}, {"table": "pacific_ocean", "columns": null}], "writes": [{"table": "defense_contractors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO infantmortalitydata (lesson_status_code, artifactname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "infantmortalitydata", "columns": ["lesson_status_code", "artifactname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO battery_storage SELECT a.billing_country, b.startup_name FROM vehiclemodels a JOIN dws.payments_delta b ON a.menucategory = b.menucategory\"\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": null}, {"table": "dws.payments_delta", "columns": null}], "writes": [{"table": "battery_storage", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO market_access SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO spacecraft_manufacturers SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT milliseconds, tot_cred FROM fabricdata LIMIT 262\")\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO erc20_transactions SELECT last_checkup_date, founded_year FROM ads.payments_di WHERE last_checkup_date > 440\")\n", "labels": {"reads": [{"table": "fabricdata", "columns": ["milliseconds", "tot_cred"]}, {"table": "ads.payments_di", "columns": ["last_checkup_date", "founded_year"]}], "writes": [{"table": "erc20_transactions", "columns": ["last_checkup_date", "founded_year"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"algorithmic_fairness_incidents\").where(\"dt = current_date()\").writeTo(\"arctic_sightings\").append()\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents", "columns": null}], "writes": [{"table": "arctic_sightings", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ethicalaibudget SELECT job_category, inspectionscore, attendance_id, destination FROM conservation_initiatives WHERE job_category > 255\"], check=True)\n", "labels": {"reads": [{"table": "conservation_initiatives", "columns": ["job_category", "inspectionscore", "attendance_id", "destination"]}], "writes": [{"table": "ethicalaibudget", "columns": ["job_category", "inspectionscore", "attendance_id", "destination"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"educators\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"trade_history\")\n", "labels": {"reads": [{"table": "educators", "columns": null}], "writes": [{"table": "trade_history", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi_shipments_daily\");\ndf.write().mode(\"overwrite\").saveAsTable(\"acidification_data\");\n", "labels": {"reads": [{"table": "bi_shipments_daily", "columns": null}], "writes": [{"table": "acidification_data", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 182;\nSQL\n", "labels": {"reads": [{"table": "happy_hour", "columns": ["fabrictype", "organized_by"]}, {"table": "dancefunding", "columns": ["total_beds", "guest_first_name"]}], "writes": [{"table": "legal_aid_organizations", "columns": ["total_beds", "guest_first_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO all_documents SELECT * FROM legacy\nspark.sql(\"INSERT INTO un_peacekeeping_operations SELECT affiliation, card_number FROM indie_artists WHERE affiliation > 330\")\n", "labels": {"reads": [{"table": "indie_artists", "columns": ["affiliation", "card_number"]}], "writes": [{"table": "un_peacekeeping_operations", "columns": ["affiliation", "card_number"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"exam_results\")\nsrc.write.insertInto(\"pollution_control_initiatives\", overwrite=True)\n", "labels": {"reads": [{"table": "exam_results", "columns": null}], "writes": [{"table": "pollution_control_initiatives", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO spacemissions SELECT * FROM legacy\ncur.execute(\"SELECT card_number, kids FROM ads.ads_campaigns_full LIMIT 167\")\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": ["card_number", "kids"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT u_id, sale_year FROM ads_payments_hourly LIMIT 157\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": ["u_id", "sale_year"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws.dws_coupon_use_full\", conn)\ndf.to_sql(\"review\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_full", "columns": null}], "writes": [{"table": "review", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO nutrition_facts SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO gardens SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"infrastructureprojects\")\nsrc.write.insertInto(\"maintenancerequests\", overwrite=True)\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": null}], "writes": [{"table": "maintenancerequests", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO winter_olympics SELECT a.drug, b.matchid FROM public.police_calls a JOIN block b ON a.station_id = b.station_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "public.police_calls", "columns": null}, {"table": "block", "columns": null}], "writes": [{"table": "winter_olympics", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT founding_location, workforce_development FROM african_union_countries\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"employeedata\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "african_union_countries", "columns": ["founding_location", "workforce_development"]}], "writes": [{"table": "employeedata", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO healthydelights SELECT a.donation_id, b.beds FROM financial_capability_program a JOIN species_forests b ON a.don_name = b.don_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "financial_capability_program", "columns": null}, {"table": "species_forests", "columns": null}], "writes": [{"table": "healthydelights", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO beauty_products SELECT * FROM legacy\ncur.execute(\"SELECT pd_id, is_safe FROM caribbeansea LIMIT 359\")\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["pd_id", "is_safe"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 146;\nSQL\n", "labels": {"reads": [{"table": "funding_rounds", "columns": ["total_spent", "attraction_type_code"]}, {"table": "circular_economy_initiatives", "columns": ["ppos", "amount_of_refund", "funding_round_id"]}], "writes": [{"table": "conditions", "columns": ["ppos", "amount_of_refund", "funding_round_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.billing_amount > 217).all()\n# src table: brands\nengine.execute(\"INSERT INTO healthcare_centers SELECT * FROM brands\")\n", "labels": {"reads": [{"table": "brands", "columns": null}], "writes": [{"table": "healthcare_centers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO mart.mart_campaigns_daily SELECT year_working, cows, program_name FROM ads.ads_users_hourly WHERE year_working > 71\"\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": ["year_working", "cows", "program_name"]}], "writes": [{"table": "mart.mart_campaigns_daily", "columns": ["year_working", "cows", "program_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT party_email, ethnicity FROM middle_east_military_spending LIMIT 467\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "middle_east_military_spending", "columns": ["party_email", "ethnicity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 318;\nSQL\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": ["staff_details", "sentence_length"]}, {"table": "multimodalhubs", "columns": ["investment_round", "fabrictype", "laborproductivity"]}], "writes": [{"table": "dapps", "columns": ["investment_round", "fabrictype", "laborproductivity"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO community_development.transactions SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"food_justice_contributors\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "food_justice_contributors", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO vehicle_registrations SELECT complaint_date, innovation_id FROM defensespending WHERE complaint_date > 288\");\n", "labels": {"reads": [{"table": "defensespending", "columns": ["complaint_date", "innovation_id"]}], "writes": [{"table": "vehicle_registrations", "columns": ["complaint_date", "innovation_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO meals SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT union_member, value_points FROM higher_ed.publications LIMIT 153\")\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO stg.refunds_daily SELECT contract_start_date, member_name, production_mwh FROM wind_energy WHERE contract_start_date > 80\")\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": ["union_member", "value_points"]}, {"table": "wind_energy", "columns": ["contract_start_date", "member_name", "production_mwh"]}], "writes": [{"table": "stg.refunds_daily", "columns": ["contract_start_date", "member_name", "production_mwh"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO certificate SELECT vegetable, shipment_year, opname, occupancy_rate FROM marine_mammals WHERE vegetable > 19\")\n", "labels": {"reads": [{"table": "marine_mammals", "columns": ["vegetable", "shipment_year", "opname", "occupancy_rate"]}], "writes": [{"table": "certificate", "columns": ["vegetable", "shipment_year", "opname", "occupancy_rate"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT lot_details, attendance_id FROM sustainable_warehouses LIMIT 111\")\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO yttrium_production SELECT project_number, label_id, has_aloe_vera, eventtype FROM operations WHERE project_number > 259\")\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": ["lot_details", "attendance_id"]}, {"table": "operations", "columns": ["project_number", "label_id", "has_aloe_vera", "eventtype"]}], "writes": [{"table": "yttrium_production", "columns": ["project_number", "label_id", "has_aloe_vera", "eventtype"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.mart_member_point_df (labor_id, established_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_member_point_df", "columns": ["labor_id", "established_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fish_suppliers\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "fish_suppliers", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT county_id, borough FROM innovation_grants\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"dispensaries\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "innovation_grants", "columns": ["county_id", "borough"]}], "writes": [{"table": "dispensaries", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO monitoring_zones SELECT scoreid, width, museum_details FROM eia_schedule WHERE scoreid > 29\")\n", "labels": {"reads": [{"table": "eia_schedule", "columns": ["scoreid", "width", "museum_details"]}], "writes": [{"table": "monitoring_zones", "columns": ["scoreid", "width", "museum_details"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT discovered_date, median_home_value FROM water_distribution\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"market_access\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "water_distribution", "columns": ["discovered_date", "median_home_value"]}], "writes": [{"table": "market_access", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO autonomousvehicleaccidents SELECT * FROM legacy\ncur.execute(\"SELECT temperature, campusfee FROM road_construction LIMIT 463\")\n", "labels": {"reads": [{"table": "road_construction", "columns": ["temperature", "campusfee"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"container_ships\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dispensaries\")\n", "labels": {"reads": [{"table": "container_ships", "columns": null}], "writes": [{"table": "dispensaries", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM rehab_centers\"\n", "labels": {"reads": [{"table": "rehab_centers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"world_heritage_sites\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "world_heritage_sites", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO match_result SELECT market_id, serving_size, coownerid, green_building_id FROM unesco_intangible_heritage WHERE market_id > 85\");\n", "labels": {"reads": [{"table": "unesco_intangible_heritage", "columns": ["market_id", "serving_size", "coownerid", "green_building_id"]}], "writes": [{"table": "match_result", "columns": ["market_id", "serving_size", "coownerid", "green_building_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.order_status_code > 461).all()\n# src table: landfill_capacity\nengine.execute(\"INSERT INTO platformg SELECT * FROM landfill_capacity\")\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": null}], "writes": [{"table": "platformg", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT is_hybrid, ai_adoption FROM fare_collection LIMIT 62\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "fare_collection", "columns": ["is_hybrid", "ai_adoption"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT eliminated_by, f_id FROM organization\", engine)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"ods.ods_sessions_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "organization", "columns": ["eliminated_by", "f_id"]}], "writes": [{"table": "ods.ods_sessions_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO investors SELECT town_city, round_date FROM cultural_competency_program WHERE town_city > 9\");\n", "labels": {"reads": [{"table": "cultural_competency_program", "columns": ["town_city", "round_date"]}], "writes": [{"table": "investors", "columns": ["town_city", "round_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model smart_city_projects depends on open_pedagogy_courses\ndbt build --select smart_city_projects --vars '{\"src\":\"open_pedagogy_courses\"}'\n", "labels": {"reads": [{"table": "open_pedagogy_courses", "columns": null}], "writes": [{"table": "smart_city_projects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT killed, milestone FROM arrivals\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"public.collected_fare\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "arrivals", "columns": ["killed", "milestone"]}], "writes": [{"table": "public.collected_fare", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO bus_routes SELECT onscholarship, next_entry_id, storeid, lastdonationdate FROM ocean_acidification WHERE onscholarship > 114\")\n", "labels": {"reads": [{"table": "ocean_acidification", "columns": ["onscholarship", "next_entry_id", "storeid", "lastdonationdate"]}], "writes": [{"table": "bus_routes", "columns": ["onscholarship", "next_entry_id", "storeid", "lastdonationdate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO hair_care_sales SELECT frequency, eliminated_by FROM unions WHERE frequency > 330\"], check=True)\n", "labels": {"reads": [{"table": "unions", "columns": ["frequency", "eliminated_by"]}], "writes": [{"table": "hair_care_sales", "columns": ["frequency", "eliminated_by"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO spacecraft_components SELECT art_id, organized_by, sodium FROM ap_budget WHERE art_id > 274\"\n", "labels": {"reads": [{"table": "ap_budget", "columns": ["art_id", "organized_by", "sodium"]}], "writes": [{"table": "spacecraft_components", "columns": ["art_id", "organized_by", "sodium"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO languagesatrisk SELECT * FROM legacy\ncur.execute(\"SELECT audienceid, ingredient_id FROM workout LIMIT 77\")\n", "labels": {"reads": [{"table": "workout", "columns": ["audienceid", "ingredient_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO coral_reefs SELECT starttime, salary FROM ref_document_status WHERE starttime > 4\")\n", "labels": {"reads": [{"table": "ref_document_status", "columns": ["starttime", "salary"]}], "writes": [{"table": "coral_reefs", "columns": ["starttime", "salary"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 354;\nEOF\n", "labels": {"reads": [{"table": "ads.vendors_delta", "columns": ["humidity", "daily_hire_cost", "initiative_name", "court_id"]}], "writes": [{"table": "manufacturers", "columns": ["humidity", "daily_hire_cost", "initiative_name", "court_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ods_shipments_df depends on biomes\ndbt build --select ods_shipments_df --vars 'source: biomes'\n", "labels": {"reads": [{"table": "biomes", "columns": null}], "writes": [{"table": "ods_shipments_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cloud_issues SELECT hometown, activity_id, menu_name FROM climate_projects WHERE hometown > 414\"\n", "labels": {"reads": [{"table": "climate_projects", "columns": ["hometown", "activity_id", "menu_name"]}], "writes": [{"table": "cloud_issues", "columns": ["hometown", "activity_id", "menu_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"service_budget\").toPandas()\ndf[[\"year_built\", \"draft_details\"]].to_sql(\"premises\", engine, index=False)\n", "labels": {"reads": [{"table": "service_budget", "columns": null}], "writes": [{"table": "premises", "columns": ["year_built", "draft_details"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dw_risk_score_daily SELECT booking_start_date, units_owned FROM medicine_enzyme_interaction WHERE booking_start_date > 232\"\n", "labels": {"reads": [{"table": "medicine_enzyme_interaction", "columns": ["booking_start_date", "units_owned"]}], "writes": [{"table": "dw_risk_score_daily", "columns": ["booking_start_date", "units_owned"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT review_rating, activity_type FROM music LIMIT 75\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "music", "columns": ["review_rating", "activity_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO fabricinventory SELECT serviceid, subscriber_type, mental_health_resource_access, job_id FROM participants_in_events WHERE serviceid > 248\"\n", "labels": {"reads": [{"table": "participants_in_events", "columns": ["serviceid", "subscriber_type", "mental_health_resource_access", "job_id"]}], "writes": [{"table": "fabricinventory", "columns": ["serviceid", "subscriber_type", "mental_health_resource_access", "job_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO harvest_permits SELECT a.user_account, b.eid FROM ads.ads_products_hourly a JOIN midwest_region b ON a.archaeologist_id = b.archaeologist_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.ads_products_hourly", "columns": null}, {"table": "midwest_region", "columns": null}], "writes": [{"table": "harvest_permits", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO bike_share SELECT * FROM legacy\nspark.sql(\"INSERT INTO pilot SELECT length_meters, deliveryid, accreditation_type, ship_id FROM canada_cosmetics_preferences WHERE length_meters > 339\")\n", "labels": {"reads": [{"table": "canada_cosmetics_preferences", "columns": ["length_meters", "deliveryid", "accreditation_type", "ship_id"]}], "writes": [{"table": "pilot", "columns": ["length_meters", "deliveryid", "accreditation_type", "ship_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO student_program_mapping SELECT class_senator_vote, end_time FROM employeedata WHERE class_senator_vote > 144\"\n", "labels": {"reads": [{"table": "employeedata", "columns": ["class_senator_vote", "end_time"]}], "writes": [{"table": "student_program_mapping", "columns": ["class_senator_vote", "end_time"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO machine (screen_mode, consumer_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "machine", "columns": ["screen_mode", "consumer_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO community_policing_events SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ods_risk_score_delta SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT make, drug_id FROM cyber_incidents LIMIT 349\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "cyber_incidents", "columns": ["make", "drug_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"vessel_incident_count\")\nupsert_to_warehouse(df, \"retail_workers_union\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": null}], "writes": [{"table": "retail_workers_union", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT user_account, workername FROM dailyapplestreams\", engine)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"investor\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dailyapplestreams", "columns": ["user_account", "workername"]}], "writes": [{"table": "investor", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dws.dws_coupon_use_hourly --columns quantitysold,saleamount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_coupon_use_hourly", "columns": ["quantitysold", "saleamount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"stg.inventory_df\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"stg_users_daily\")\n", "labels": {"reads": [{"table": "stg.inventory_df", "columns": null}], "writes": [{"table": "stg_users_daily", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table tv_shows --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tv_shows", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"immunization\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "immunization", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM militarydrones\", conn)\ndf.to_sql(\"song\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "militarydrones", "columns": null}], "writes": [{"table": "song", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM high_risk\"\n", "labels": {"reads": [{"table": "high_risk", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table recycling_stats --columns org,oil_volume --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "recycling_stats", "columns": ["org", "oil_volume"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.clinic_id > 346).all()\n# src table: haircare_sales\nengine.execute(\"INSERT INTO courses SELECT * FROM haircare_sales\")\n", "labels": {"reads": [{"table": "haircare_sales", "columns": null}], "writes": [{"table": "courses", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"agroecology_practices\")\npersist_to_warehouse(df, \"local_impact\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "agroecology_practices", "columns": null}], "writes": [{"table": "local_impact", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"investment\").toPandas()\ndf[[\"algorithm_name\", \"bats\"]].to_sql(\"arrivals\", engine, index=False)\n", "labels": {"reads": [{"table": "investment", "columns": null}], "writes": [{"table": "arrivals", "columns": ["algorithm_name", "bats"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"gamesessions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "gamesessions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"performers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"textileworkers\")\n", "labels": {"reads": [{"table": "performers", "columns": null}], "writes": [{"table": "textileworkers", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"genetics.projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "genetics.projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"maintenance_contracts\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"bi.member_point_full\")\n", "labels": {"reads": [{"table": "maintenance_contracts", "columns": null}], "writes": [{"table": "bi.member_point_full", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO disabilitysupportprograms SELECT mappingid, restaurant_id FROM cuisine WHERE mappingid > 486\")\n", "labels": {"reads": [{"table": "cuisine", "columns": ["mappingid", "restaurant_id"]}], "writes": [{"table": "disabilitysupportprograms", "columns": ["mappingid", "restaurant_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dysprosiumproduction SELECT therapy_sessions, advisoryid, building, fundingdate FROM climate_projects WHERE therapy_sessions > 494\"], check=True)\n", "labels": {"reads": [{"table": "climate_projects", "columns": ["therapy_sessions", "advisoryid", "building", "fundingdate"]}], "writes": [{"table": "dysprosiumproduction", "columns": ["therapy_sessions", "advisoryid", "building", "fundingdate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO mart.risk_score_df SELECT wage, crs_code, detention_type_code FROM public.trips_by_day_train WHERE wage > 198\")\n", "labels": {"reads": [{"table": "public.trips_by_day_train", "columns": ["wage", "crs_code", "detention_type_code"]}], "writes": [{"table": "mart.risk_score_df", "columns": ["wage", "crs_code", "detention_type_code"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model region_stats depends on characteristics\ndbt build --select region_stats --vars 'source: characteristics'\n", "labels": {"reads": [{"table": "characteristics", "columns": null}], "writes": [{"table": "region_stats", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT handling_id, visit_details FROM rental LIMIT 182\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO transportation SELECT certification, funding_amount, stu_dob FROM geological_survey WHERE certification > 317\")\n", "labels": {"reads": [{"table": "rental", "columns": ["handling_id", "visit_details"]}, {"table": "geological_survey", "columns": ["certification", "funding_amount", "stu_dob"]}], "writes": [{"table": "transportation", "columns": ["certification", "funding_amount", "stu_dob"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO initiatives (cname, start_station_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "initiatives", "columns": ["cname", "start_station_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"auto_shows\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"vaccine_administered\")\n", "labels": {"reads": [{"table": "auto_shows", "columns": null}], "writes": [{"table": "vaccine_administered", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model atlantic_ocean_fish depends on ai_safety_incidents\ndbt run --models atlantic_ocean_fish --vars '{\"src\":\"ai_safety_incidents\"}'\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": null}], "writes": [{"table": "atlantic_ocean_fish", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT publication_date, user_account FROM participants_in_events LIMIT 486\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO onlineengagement SELECT problem_id, book_club_id, prof_office, numhearings FROM user_genre WHERE problem_id > 396\")\n", "labels": {"reads": [{"table": "participants_in_events", "columns": ["publication_date", "user_account"]}, {"table": "user_genre", "columns": ["problem_id", "book_club_id", "prof_office", "numhearings"]}], "writes": [{"table": "onlineengagement", "columns": ["problem_id", "book_club_id", "prof_office", "numhearings"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.effort_name > 467).all()\n# src table: pets\nengine.execute(\"INSERT INTO labour_productivity SELECT * FROM pets\")\n", "labels": {"reads": [{"table": "pets", "columns": null}], "writes": [{"table": "labour_productivity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sitem\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "sitem", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"hospital_visits\")\nsrc.write.insertInto(\"bi.bi_events_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "hospital_visits", "columns": null}], "writes": [{"table": "bi.bi_events_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"articles_es\")\npersist_to_output(df, \"marketing_regions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "articles_es", "columns": null}], "writes": [{"table": "marketing_regions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO biosensor.patents SELECT num_libraries, sale_date, inspectiondate FROM order_items WHERE num_libraries > 287\");\n", "labels": {"reads": [{"table": "order_items", "columns": ["num_libraries", "sale_date", "inspectiondate"]}], "writes": [{"table": "biosensor.patents", "columns": ["num_libraries", "sale_date", "inspectiondate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM carbon_emissions\"\n", "labels": {"reads": [{"table": "carbon_emissions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO military_aircraft_maintenance SELECT star_rating_description, num_workers FROM device_usage WHERE star_rating_description > 413\");\n", "labels": {"reads": [{"table": "device_usage", "columns": ["star_rating_description", "num_workers"]}], "writes": [{"table": "military_aircraft_maintenance", "columns": ["star_rating_description", "num_workers"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO bi.bi_sessions_hourly SELECT preference_score, all_games, license_number FROM bi.bi_payments_full WHERE preference_score > 40\");\n", "labels": {"reads": [{"table": "bi.bi_payments_full", "columns": ["preference_score", "all_games", "license_number"]}], "writes": [{"table": "bi.bi_sessions_hourly", "columns": ["preference_score", "all_games", "license_number"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"safety_incident\").where(\"dt = current_date()\").writeTo(\"oil_production\").append()\n", "labels": {"reads": [{"table": "safety_incident", "columns": null}], "writes": [{"table": "oil_production", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 477;\nEOF\n", "labels": {"reads": [{"table": "state_budget", "columns": ["contributorid", "quantity", "gross_in_dollar", "strategy"]}], "writes": [{"table": "inmates", "columns": ["contributorid", "quantity", "gross_in_dollar", "strategy"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO schoolc SELECT floor_area_m2, country_of_origin FROM maintenance_contracts WHERE floor_area_m2 > 361\"\n", "labels": {"reads": [{"table": "maintenance_contracts", "columns": ["floor_area_m2", "country_of_origin"]}], "writes": [{"table": "schoolc", "columns": ["floor_area_m2", "country_of_origin"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table research_grants --columns num_virtual_tours,birth_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "research_grants", "columns": ["num_virtual_tours", "birth_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"station_crime_rates\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"faculty\")\n", "labels": {"reads": [{"table": "station_crime_rates", "columns": null}], "writes": [{"table": "faculty", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model fertilizer depends on energy_consumption\ndbt build --select fertilizer --vars '{\"src\":\"energy_consumption\"}'\n", "labels": {"reads": [{"table": "energy_consumption", "columns": null}], "writes": [{"table": "fertilizer", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"workout_data\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "workout_data", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart_refunds SELECT feb, dish_name FROM climate_projects WHERE feb > 461\"\n", "labels": {"reads": [{"table": "climate_projects", "columns": ["feb", "dish_name"]}], "writes": [{"table": "mart_refunds", "columns": ["feb", "dish_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO energy_prices SELECT arrival, instrument, impactid FROM space_missions WHERE arrival > 408\")\n", "labels": {"reads": [{"table": "space_missions", "columns": ["arrival", "instrument", "impactid"]}], "writes": [{"table": "energy_prices", "columns": ["arrival", "instrument", "impactid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model tours depends on community_development.transactions\ndbt build -s tours --vars '{\"src\":\"community_development.transactions\"}'\n", "labels": {"reads": [{"table": "community_development.transactions", "columns": null}], "writes": [{"table": "tours", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO lead_mines (dst_apid, status_of_thing_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "lead_mines", "columns": ["dst_apid", "status_of_thing_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vendors\").toPandas()\ndf[[\"committee\", \"building_type\"]].to_sql(\"sustainable_warehouses\", engine, index=False)\n", "labels": {"reads": [{"table": "vendors", "columns": null}], "writes": [{"table": "sustainable_warehouses", "columns": ["committee", "building_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO experts SELECT time_minute, manufacturerid FROM demographics WHERE time_minute > 352\");\n", "labels": {"reads": [{"table": "demographics", "columns": ["time_minute", "manufacturerid"]}], "writes": [{"table": "experts", "columns": ["time_minute", "manufacturerid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 212;\nEOF\n", "labels": {"reads": [{"table": "county", "columns": ["billingcountry", "community", "company", "startdate"]}], "writes": [{"table": "country_waste_generation", "columns": ["billingcountry", "community", "company", "startdate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT trend_id, menu_item_id FROM support_programs LIMIT 123\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [{"table": "support_programs", "columns": ["trend_id", "menu_item_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO water_treatment_facilities SELECT * FROM legacy\ncur.execute(\"SELECT restock_date, participatedinesports FROM bi.bi_orders_hourly LIMIT 387\")\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": ["restock_date", "participatedinesports"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO purchases SELECT policyname, classroom FROM broadband_revenue WHERE policyname > 342\");\n", "labels": {"reads": [{"table": "broadband_revenue", "columns": ["policyname", "classroom"]}], "writes": [{"table": "purchases", "columns": ["policyname", "classroom"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.dwd_vendors\", conn)\ndf.to_sql(\"missions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": null}], "writes": [{"table": "missions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT grant_amount, tourist_details FROM emergencyservices LIMIT 228\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO cuisine SELECT task_details, grant_end_date FROM threat_severity WHERE task_details > 70\")\n", "labels": {"reads": [{"table": "emergencyservices", "columns": ["grant_amount", "tourist_details"]}, {"table": "threat_severity", "columns": ["task_details", "grant_end_date"]}], "writes": [{"table": "cuisine", "columns": ["task_details", "grant_end_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO shark_biomass SELECT mountain_id, conferenceid, sustainablepractices, char_cells FROM high_risk WHERE mountain_id > 270\");\n", "labels": {"reads": [{"table": "high_risk", "columns": ["mountain_id", "conferenceid", "sustainablepractices", "char_cells"]}], "writes": [{"table": "shark_biomass", "columns": ["mountain_id", "conferenceid", "sustainablepractices", "char_cells"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO product_characteristics SELECT cust_name, visitid, connection, field FROM debris WHERE cust_name > 214\")\n", "labels": {"reads": [{"table": "debris", "columns": ["cust_name", "visitid", "connection", "field"]}], "writes": [{"table": "product_characteristics", "columns": ["cust_name", "visitid", "connection", "field"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO public_participation (num_of_factories, stationname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "public_participation", "columns": ["num_of_factories", "stationname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT currency, event_type_id FROM manager_award\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"rural_clinics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "manager_award", "columns": ["currency", "event_type_id"]}], "writes": [{"table": "rural_clinics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ref_hotel_star_ratings depends on funding_records\ndbt run --select ref_hotel_star_ratings --vars '{\"src\":\"funding_records\"}'\n", "labels": {"reads": [{"table": "funding_records", "columns": null}], "writes": [{"table": "ref_hotel_star_ratings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO human_resources SELECT payment_method, postal_code, playergameid, province_id FROM section WHERE payment_method > 302\"\n", "labels": {"reads": [{"table": "section", "columns": ["payment_method", "postal_code", "playergameid", "province_id"]}], "writes": [{"table": "human_resources", "columns": ["payment_method", "postal_code", "playergameid", "province_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO measurements SELECT * FROM legacy\nspark.sql(\"INSERT INTO dws.exposure_df SELECT start_date, drill_count, round_date, fleet_name FROM container_ships WHERE start_date > 224\")\n", "labels": {"reads": [{"table": "container_ships", "columns": ["start_date", "drill_count", "round_date", "fleet_name"]}], "writes": [{"table": "dws.exposure_df", "columns": ["start_date", "drill_count", "round_date", "fleet_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.orders_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.inventory_hourly\")\n", "labels": {"reads": [{"table": "ads.orders_daily", "columns": null}], "writes": [{"table": "mart.inventory_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO equipment_sales SELECT a.publication_year, b.image_data FROM stg.cart_item_full a JOIN mart.mart_campaigns_daily b ON a.statement_id = b.statement_id\"\n", "labels": {"reads": [{"table": "stg.cart_item_full", "columns": null}, {"table": "mart.mart_campaigns_daily", "columns": null}], "writes": [{"table": "equipment_sales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ods.ods_risk_score_df\").where(\"dt = current_date()\").writeTo(\"circuits\").append()\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": null}], "writes": [{"table": "circuits", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"vessel_registry\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"routes\")\n", "labels": {"reads": [{"table": "vessel_registry", "columns": null}], "writes": [{"table": "routes", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO dailystreams SELECT emission_date, genreid, satelliteid, mineid FROM enrolled_in WHERE emission_date > 304\");\n", "labels": {"reads": [{"table": "enrolled_in", "columns": ["emission_date", "genreid", "satelliteid", "mineid"]}], "writes": [{"table": "dailystreams", "columns": ["emission_date", "genreid", "satelliteid", "mineid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 397;\nSQL\n", "labels": {"reads": [{"table": "mexico_regions", "columns": ["screening", "installation_date"]}, {"table": "ocean_acidification", "columns": ["stop", "plant", "lipstick_id", "squadron"]}], "writes": [{"table": "biosensors.projects", "columns": ["stop", "plant", "lipstick_id", "squadron"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 7;\nEOF\n", "labels": {"reads": [{"table": "dws.payments_delta", "columns": ["subscribe_date", "rate"]}], "writes": [{"table": "salary", "columns": ["subscribe_date", "rate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO russia_nato_diplomacy SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO fields_production SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO dwd.products_di SELECT money_requested, pallet_id, socialimpactscore, subscriber_id FROM erc20_transactions WHERE money_requested > 252\")\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": ["money_requested", "pallet_id", "socialimpactscore", "subscriber_id"]}], "writes": [{"table": "dwd.products_di", "columns": ["money_requested", "pallet_id", "socialimpactscore", "subscriber_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"rd_expenditure\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "rd_expenditure", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"soilmoisturedata\").where(\"dt = current_date()\").writeTo(\"spacecraft\").append()\n", "labels": {"reads": [{"table": "soilmoisturedata", "columns": null}], "writes": [{"table": "spacecraft", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM cultural_competency_training\", conn)\ndf.to_sql(\"dw.member_point_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "cultural_competency_training", "columns": null}], "writes": [{"table": "dw.member_point_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO digital_divide_initiatives SELECT streamid, programtype FROM assessment_notes WHERE streamid > 489\")\n", "labels": {"reads": [{"table": "assessment_notes", "columns": ["streamid", "programtype"]}], "writes": [{"table": "digital_divide_initiatives", "columns": ["streamid", "programtype"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model artprograms depends on volunteer_hours\ndbt run -s artprograms --vars '{\"source_table\":\"volunteer_hours\"}'\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": null}], "writes": [{"table": "artprograms", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO carbon_offset_projects (products_this_year, journalist_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "carbon_offset_projects", "columns": ["products_this_year", "journalist_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ca_menu_items\").toPandas()\ndf[[\"sales_details\", \"contactid\"]].to_sql(\"bi_products\", engine, index=False)\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": null}], "writes": [{"table": "bi_products", "columns": ["sales_details", "contactid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"athlete_wellbeing\");\ndf.write().mode(\"overwrite\").saveAsTable(\"marine_species_indian\");\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": null}], "writes": [{"table": "marine_species_indian", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"employees\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "employees", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO arcticwildlifereserve SELECT consultations, document_description, user_login, funding_received FROM farmers_india WHERE consultations > 394\"\n", "labels": {"reads": [{"table": "farmers_india", "columns": ["consultations", "document_description", "user_login", "funding_received"]}], "writes": [{"table": "arcticwildlifereserve", "columns": ["consultations", "document_description", "user_login", "funding_received"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT fiscal_year, startyear FROM district_schools\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"conservation_initiatives\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "district_schools", "columns": ["fiscal_year", "startyear"]}], "writes": [{"table": "conservation_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM inspections\"\n", "labels": {"reads": [{"table": "inspections", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO retailers SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table indian_ocean_fishingvessels --columns product_category,union_members --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "indian_ocean_fishingvessels", "columns": ["product_category", "union_members"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO studies SELECT dphone, led_by FROM dwd.dwd_campaigns WHERE dphone > 128\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns", "columns": ["dphone", "led_by"]}], "writes": [{"table": "studies", "columns": ["dphone", "led_by"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vessel_performance SELECT stationid, threat_type, salary, height FROM visitordemographics WHERE stationid > 386\"\n", "labels": {"reads": [{"table": "visitordemographics", "columns": ["stationid", "threat_type", "salary", "height"]}], "writes": [{"table": "vessel_performance", "columns": ["stationid", "threat_type", "salary", "height"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 326;\nEOF\n", "labels": {"reads": [{"table": "marketingbudget", "columns": ["employee_id", "bioprocess_name", "genre"]}], "writes": [{"table": "influencers", "columns": ["employee_id", "bioprocess_name", "genre"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO studentaccommodations SELECT a.improvement, b.member_name FROM attribute_definitions a JOIN cosmetic_sales b ON a.denomination = b.denomination\"\n", "labels": {"reads": [{"table": "attribute_definitions", "columns": null}, {"table": "cosmetic_sales", "columns": null}], "writes": [{"table": "studentaccommodations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO part_faults SELECT circuitid, amountdonated, dept_id FROM school_districts WHERE circuitid > 102\");\n", "labels": {"reads": [{"table": "school_districts", "columns": ["circuitid", "amountdonated", "dept_id"]}], "writes": [{"table": "part_faults", "columns": ["circuitid", "amountdonated", "dept_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd.dwd_campaigns_df\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO water_conservation_brazil SELECT a.phone_id, b.energy_consumption FROM mediators a JOIN project_timeline b ON a.time_of_purchase = b.time_of_purchase\"\n", "labels": {"reads": [{"table": "mediators", "columns": null}, {"table": "project_timeline", "columns": null}], "writes": [{"table": "water_conservation_brazil", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO streams SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sales_id, tech FROM communityengagement LIMIT 51\")\nimport logging\nspark.sql(\"INSERT INTO dwd.dwd_device_log_delta SELECT organizationname, batting_average, adults, song_name FROM wholesale_orders WHERE organizationname > 190\")\n", "labels": {"reads": [{"table": "communityengagement", "columns": ["sales_id", "tech"]}, {"table": "wholesale_orders", "columns": ["organizationname", "batting_average", "adults", "song_name"]}], "writes": [{"table": "dwd.dwd_device_log_delta", "columns": ["organizationname", "batting_average", "adults", "song_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO projects SELECT card_number, date_claim_settled, mentalhealthscore, trend FROM draft_copies WHERE card_number > 166\"], check=True)\n", "labels": {"reads": [{"table": "draft_copies", "columns": ["card_number", "date_claim_settled", "mentalhealthscore", "trend"]}], "writes": [{"table": "projects", "columns": ["card_number", "date_claim_settled", "mentalhealthscore", "trend"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"issues\")\nsave_to_warehouse(df, \"city.community_policing\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "issues", "columns": null}], "writes": [{"table": "city.community_policing", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO view_unit_status SELECT deliverydate, mentalhealthscore FROM circulation_history WHERE deliverydate > 20\"\n", "labels": {"reads": [{"table": "circulation_history", "columns": ["deliverydate", "mentalhealthscore"]}], "writes": [{"table": "view_unit_status", "columns": ["deliverydate", "mentalhealthscore"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"wastewater_plants\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.quantityproduced > 485).all()\n# src table: stg_orders_hourly\nengine.execute(\"INSERT INTO attendees SELECT * FROM stg_orders_hourly\")\n", "labels": {"reads": [{"table": "stg_orders_hourly", "columns": null}], "writes": [{"table": "attendees", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_cart_item\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws_cart_item", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO gamedesigndata (goal_id, operationid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "gamedesigndata", "columns": ["goal_id", "operationid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.location_id > 400).all()\n# src table: pitstops\nengine.execute(\"INSERT INTO seeds SELECT * FROM pitstops\")\n", "labels": {"reads": [{"table": "pitstops", "columns": null}], "writes": [{"table": "seeds", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"jobs\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "jobs", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO ma_inspections SELECT employee_count, classroom FROM al_jazeera_data WHERE employee_count > 155\")\n", "labels": {"reads": [{"table": "al_jazeera_data", "columns": ["employee_count", "classroom"]}], "writes": [{"table": "ma_inspections", "columns": ["employee_count", "classroom"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ods.sessions_daily\", conn)\ndf.to_sql(\"article_views\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": null}], "writes": [{"table": "article_views", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_table(ctx, \"dws.dws_member_point_df\")\ndump_to_store(df, \"representative\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": null}], "writes": [{"table": "representative", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO item SELECT cell_mobile_phone_number, mailing_date FROM waterconservationinitiatives WHERE cell_mobile_phone_number > 66\")\n", "labels": {"reads": [{"table": "waterconservationinitiatives", "columns": ["cell_mobile_phone_number", "mailing_date"]}], "writes": [{"table": "item", "columns": ["cell_mobile_phone_number", "mailing_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sustainable_urban_properties_2\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws.dws_orders\")\n", "labels": {"reads": [{"table": "sustainable_urban_properties_2", "columns": null}], "writes": [{"table": "dws.dws_orders", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.mart_coupon_use_delta (num_investments, purchaseid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_coupon_use_delta", "columns": ["num_investments", "purchaseid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.event_details > 291).all()\n# src table: consumer_preference\nengine.execute(\"INSERT INTO sportsinfo SELECT * FROM consumer_preference\")\n", "labels": {"reads": [{"table": "consumer_preference", "columns": null}], "writes": [{"table": "sportsinfo", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT asset_model, goal_id FROM diversity LIMIT 346\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "diversity", "columns": ["asset_model", "goal_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 146;\nSQL\n", "labels": {"reads": [{"table": "staff_members", "columns": ["mappingid", "issue_date"]}, {"table": "stg.stg_users_di", "columns": ["hourlyrate", "loan_amount", "age_group_id", "driver_id"]}], "writes": [{"table": "donationsbycause", "columns": ["hourlyrate", "loan_amount", "age_group_id", "driver_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 425;\nEOF\n", "labels": {"reads": [{"table": "packages", "columns": ["image_data", "donor_country"]}], "writes": [{"table": "hair_care_sales", "columns": ["image_data", "donor_country"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"browser\")\nupsert_to_output(df, \"broadband_revenue\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "browser", "columns": null}], "writes": [{"table": "broadband_revenue", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO rural_projects SELECT communityname, contract_id FROM volunteerprograms WHERE communityname > 87\"\n", "labels": {"reads": [{"table": "volunteerprograms", "columns": ["communityname", "contract_id"]}], "writes": [{"table": "rural_projects", "columns": ["communityname", "contract_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table status --target-dir /tmp/land\n", "labels": {"reads": [{"table": "status", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_items\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"rural_infrastructure\")\n", "labels": {"reads": [{"table": "food_items", "columns": null}], "writes": [{"table": "rural_infrastructure", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"race_ethnicity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"nailpolishsales\")\n", "labels": {"reads": [{"table": "race_ethnicity", "columns": null}], "writes": [{"table": "nailpolishsales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"inmates\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"city_department\")\n", "labels": {"reads": [{"table": "inmates", "columns": null}], "writes": [{"table": "city_department", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fans_merchandise_basketball SELECT practice, region, pid FROM ship_agent WHERE practice > 291\"], check=True)\n", "labels": {"reads": [{"table": "ship_agent", "columns": ["practice", "region", "pid"]}], "writes": [{"table": "fans_merchandise_basketball", "columns": ["practice", "region", "pid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO price_data SELECT member, musical_id, quantity, brand_mentioned FROM medical_facilities WHERE member > 200\"\n", "labels": {"reads": [{"table": "medical_facilities", "columns": ["member", "musical_id", "quantity", "brand_mentioned"]}], "writes": [{"table": "price_data", "columns": ["member", "musical_id", "quantity", "brand_mentioned"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ecohousing depends on residents_services\ndbt build --select ecohousing --vars '{\"src\":\"residents_services\"}'\n", "labels": {"reads": [{"table": "residents_services", "columns": null}], "writes": [{"table": "ecohousing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"trips\");\ndf.write().mode(\"overwrite\").saveAsTable(\"operation\");\n", "labels": {"reads": [{"table": "trips", "columns": null}], "writes": [{"table": "operation", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO ads_orders SELECT debate_id, annual_entry_exit FROM dwd.coupon_use_daily WHERE debate_id > 29\")\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": ["debate_id", "annual_entry_exit"]}], "writes": [{"table": "ads_orders", "columns": ["debate_id", "annual_entry_exit"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO military_expenditure (hardware_model_name, judge_state) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "military_expenditure", "columns": ["hardware_model_name", "judge_state"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO support_programs (area_type, date_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "support_programs", "columns": ["area_type", "date_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"financial_capability\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "financial_capability", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 50;\nEOF\n", "labels": {"reads": [{"table": "waste", "columns": ["date_id", "budget_type_code", "employmentdate", "assessmentdate"]}], "writes": [{"table": "scientists", "columns": ["date_id", "budget_type_code", "employmentdate", "assessmentdate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 27;\nSQL\n", "labels": {"reads": [{"table": "asia_events", "columns": ["investors", "document_structure_description"]}, {"table": "dw.dw_products_delta", "columns": ["shop_name", "mean_sea_level_pressure_inches"]}], "writes": [{"table": "invoice", "columns": ["shop_name", "mean_sea_level_pressure_inches"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi.payments_daily\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "bi.payments_daily", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"support_programs\").where(\"dt = current_date()\").writeTo(\"swimmer\").append()\n", "labels": {"reads": [{"table": "support_programs", "columns": null}], "writes": [{"table": "swimmer", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM africa_schema.african_mines\"\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 472;\nSQL\n", "labels": {"reads": [{"table": "biotech.startups", "columns": ["year_working", "event_type_id"]}, {"table": "fabricdata", "columns": ["person_name", "operationid", "school_id", "extraction_date"]}], "writes": [{"table": "stg.campaigns_daily", "columns": ["person_name", "operationid", "school_id", "extraction_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO coralreefs SELECT effort_name, noise_level, date_assigned_from, clinic_type FROM shoes WHERE effort_name > 371\"\n", "labels": {"reads": [{"table": "shoes", "columns": ["effort_name", "noise_level", "date_assigned_from", "clinic_type"]}], "writes": [{"table": "coralreefs", "columns": ["effort_name", "noise_level", "date_assigned_from", "clinic_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ads.ads_users_hourly SELECT 1\"\nlogger.info(msg)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model basketball_teams depends on medical_professionals\ndbt build --models basketball_teams --vars 'source: medical_professionals'\n", "labels": {"reads": [{"table": "medical_professionals", "columns": null}], "writes": [{"table": "basketball_teams", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.device_log_df\"\n", "labels": {"reads": [{"table": "stg.device_log_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dwd.dwd_campaigns SELECT * FROM legacy\nspark.sql(\"INSERT INTO communitydevelopment SELECT violation_id, built, emp_dob FROM transportation WHERE violation_id > 489\")\n", "labels": {"reads": [{"table": "transportation", "columns": ["violation_id", "built", "emp_dob"]}], "writes": [{"table": "communitydevelopment", "columns": ["violation_id", "built", "emp_dob"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO mart_shipments_full SELECT membergender, community_members, check_in_date FROM southchinasea.wells WHERE membergender > 415\"\n", "labels": {"reads": [{"table": "southchinasea.wells", "columns": ["membergender", "community_members", "check_in_date"]}], "writes": [{"table": "mart_shipments_full", "columns": ["membergender", "community_members", "check_in_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"submarine_canyons\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"manufacturersustainability\")\n", "labels": {"reads": [{"table": "submarine_canyons", "columns": null}], "writes": [{"table": "manufacturersustainability", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO aid_missions SELECT incident_description, productname FROM stg.refunds_daily WHERE incident_description > 300\")\n", "labels": {"reads": [{"table": "stg.refunds_daily", "columns": ["incident_description", "productname"]}], "writes": [{"table": "aid_missions", "columns": ["incident_description", "productname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO tournaments SELECT style, shipping_mode, hourid FROM video_content WHERE style > 460\")\n", "labels": {"reads": [{"table": "video_content", "columns": ["style", "shipping_mode", "hourid"]}], "writes": [{"table": "tournaments", "columns": ["style", "shipping_mode", "hourid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 239;\nEOF\n", "labels": {"reads": [{"table": "plots", "columns": ["transaction_type_description", "water_depth"]}], "writes": [{"table": "date", "columns": ["transaction_type_description", "water_depth"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO article_views SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO landfill_capacity_city_v2 SELECT a.provider_id, b.train_id FROM animals a JOIN workforce_development b ON a.plant = b.plant\"\n", "labels": {"reads": [{"table": "animals", "columns": null}, {"table": "workforce_development", "columns": null}], "writes": [{"table": "landfill_capacity_city_v2", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tech_accessibility_funding\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"impact_asia\")\nupsert_to_output(df, \"safetyincidents\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "impact_asia", "columns": null}], "writes": [{"table": "safetyincidents", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO contractnegotiations SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO agroecology_practices (horizontal_bar_points, customer_type_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "agroecology_practices", "columns": ["horizontal_bar_points", "customer_type_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ocean_basins SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"extraction_methods\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"community_health_centers\")\n", "labels": {"reads": [{"table": "extraction_methods", "columns": null}], "writes": [{"table": "community_health_centers", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO platformi SELECT cost_id, asset_name, claimdate, staff_gender FROM tourismproviders WHERE cost_id > 301\"\n", "labels": {"reads": [{"table": "tourismproviders", "columns": ["cost_id", "asset_name", "claimdate", "staff_gender"]}], "writes": [{"table": "platformi", "columns": ["cost_id", "asset_name", "claimdate", "staff_gender"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"trainers\")\nsave_to_target(df, \"educators\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "trainers", "columns": null}], "writes": [{"table": "educators", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO doctors (exploited, call_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "doctors", "columns": ["exploited", "call_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 435;\nEOF\n", "labels": {"reads": [{"table": "dwd.dwd_payments_full", "columns": ["createdate", "participation_date", "warehouse_id"]}], "writes": [{"table": "urban_farms", "columns": ["createdate", "participation_date", "warehouse_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO models_safety (shelter_id, spent) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "models_safety", "columns": ["shelter_id", "spent"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"states\")\nsrc.write.insertInto(\"country_labor\", overwrite=True)\n", "labels": {"reads": [{"table": "states", "columns": null}], "writes": [{"table": "country_labor", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM artist_concerts\", conn)\ndf.to_sql(\"makeup_sales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "artist_concerts", "columns": null}], "writes": [{"table": "makeup_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO interventions SELECT indigenous, update_date, shipping_agent_name, material FROM daily_oil_production WHERE indigenous > 182\"\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": ["indigenous", "update_date", "shipping_agent_name", "material"]}], "writes": [{"table": "interventions", "columns": ["indigenous", "update_date", "shipping_agent_name", "material"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO instructors SELECT shippingmethod, class_room, hardware_colours, airport_name FROM parties WHERE shippingmethod > 143\")\n", "labels": {"reads": [{"table": "parties", "columns": ["shippingmethod", "class_room", "hardware_colours", "airport_name"]}], "writes": [{"table": "instructors", "columns": ["shippingmethod", "class_room", "hardware_colours", "airport_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"office_locations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "office_locations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO life_expectancy SELECT a.threat_type, b.hotel_chain_id FROM flights a JOIN bioprocess.engineering_projects b ON a.destinationid = b.destinationid\"\n", "labels": {"reads": [{"table": "flights", "columns": null}, {"table": "bioprocess.engineering_projects", "columns": null}], "writes": [{"table": "life_expectancy", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO mart.inventory_hourly SELECT case_id, retailer_id, project_name, occupation FROM economic_diversification WHERE case_id > 420\");\n", "labels": {"reads": [{"table": "economic_diversification", "columns": ["case_id", "retailer_id", "project_name", "occupation"]}], "writes": [{"table": "mart.inventory_hourly", "columns": ["case_id", "retailer_id", "project_name", "occupation"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT routename, productivity FROM domesticconferences LIMIT 235\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "domesticconferences", "columns": ["routename", "productivity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO bi.bi_orders_daily SELECT document_name, coal_reserve_remaining, vote_percent, is_valid FROM infra_diversification WHERE document_name > 103\")\n", "labels": {"reads": [{"table": "infra_diversification", "columns": ["document_name", "coal_reserve_remaining", "vote_percent", "is_valid"]}], "writes": [{"table": "bi.bi_orders_daily", "columns": ["document_name", "coal_reserve_remaining", "vote_percent", "is_valid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO complaints SELECT a.host_country, b.crop FROM teacher_professional_development a JOIN management b ON a.therapy_id = b.therapy_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "teacher_professional_development", "columns": null}, {"table": "management", "columns": null}], "writes": [{"table": "complaints", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO volunteerhours SELECT * FROM legacy\nspark.sql(\"INSERT INTO historicalcontexts SELECT indigenous, is_autonomous FROM ads.ads_cart_item_hourly WHERE indigenous > 288\")\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": ["indigenous", "is_autonomous"]}], "writes": [{"table": "historicalcontexts", "columns": ["indigenous", "is_autonomous"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_table(ctx, \"exhibition_visits\")\npersist_to_store(df, \"atlantic_ocean\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": null}], "writes": [{"table": "atlantic_ocean", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO solana_transactions SELECT vegan, shipmentid, lot_details FROM dw.dw_member_point_di WHERE vegan > 347\"\n", "labels": {"reads": [{"table": "dw.dw_member_point_di", "columns": ["vegan", "shipmentid", "lot_details"]}], "writes": [{"table": "solana_transactions", "columns": ["vegan", "shipmentid", "lot_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sales SELECT a.tech, b.time_month FROM election a JOIN gamesessions b ON a.founder_race = b.founder_race\"\n", "labels": {"reads": [{"table": "election", "columns": null}, {"table": "gamesessions", "columns": null}], "writes": [{"table": "sales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 471;\nEOF\n", "labels": {"reads": [{"table": "regional_archaeologists", "columns": ["asset_disposed_date", "contractid", "crs_credit"]}], "writes": [{"table": "recycling_rates_state", "columns": ["asset_disposed_date", "contractid", "crs_credit"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO digital_trends SELECT * FROM legacy\nspark.sql(\"INSERT INTO ocean_floor SELECT hours_served, focal_length_mm FROM community_programs WHERE hours_served > 101\")\n", "labels": {"reads": [{"table": "community_programs", "columns": ["hours_served", "focal_length_mm"]}], "writes": [{"table": "ocean_floor", "columns": ["hours_served", "focal_length_mm"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO innovation_trends SELECT cuisine, asset_acquired_date, main_industry FROM bi.bi_orders_daily WHERE cuisine > 422\")\n", "labels": {"reads": [{"table": "bi.bi_orders_daily", "columns": ["cuisine", "asset_acquired_date", "main_industry"]}], "writes": [{"table": "innovation_trends", "columns": ["cuisine", "asset_acquired_date", "main_industry"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table daily_transaction_volume --target-dir /tmp/land\n", "labels": {"reads": [{"table": "daily_transaction_volume", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO conservation_programs SELECT * FROM legacy\nspark.sql(\"INSERT INTO bike_share SELECT vaccinations, providerid, plantid FROM dancefunding WHERE vaccinations > 26\")\n", "labels": {"reads": [{"table": "dancefunding", "columns": ["vaccinations", "providerid", "plantid"]}], "writes": [{"table": "bike_share", "columns": ["vaccinations", "providerid", "plantid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT incident_description, is_ev FROM street_markets\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"lots\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "street_markets", "columns": ["incident_description", "is_ev"]}], "writes": [{"table": "lots", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ods.ods_campaigns_hourly SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 402;\nSQL\n", "labels": {"reads": [{"table": "farmers", "columns": ["investmentid", "retweets"]}, {"table": "wastewater_treatment", "columns": ["spacecraft_name", "quantity_containers", "hourdate", "organisation_type"]}], "writes": [{"table": "epl_teams", "columns": ["spacecraft_name", "quantity_containers", "hourdate", "organisation_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO agricultural_innovations SELECT wastetype, employmentdate, local, dormid FROM operations WHERE wastetype > 244\");\n", "labels": {"reads": [{"table": "operations", "columns": ["wastetype", "employmentdate", "local", "dormid"]}], "writes": [{"table": "agricultural_innovations", "columns": ["wastetype", "employmentdate", "local", "dormid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT ai_model, project_education FROM libraries LIMIT 364\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO audience SELECT donor_program, park_id, faculty FROM artsheritage WHERE donor_program > 484\")\n", "labels": {"reads": [{"table": "libraries", "columns": ["ai_model", "project_education"]}, {"table": "artsheritage", "columns": ["donor_program", "park_id", "faculty"]}], "writes": [{"table": "audience", "columns": ["donor_program", "park_id", "faculty"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.issue_month > 458).all()\n# src table: bi.inventory_daily\nengine.execute(\"INSERT INTO submission SELECT * FROM bi.inventory_daily\")\n", "labels": {"reads": [{"table": "bi.inventory_daily", "columns": null}], "writes": [{"table": "submission", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"auto_show\").toPandas()\ndf[[\"worker_id\", \"total_points\"]].to_sql(\"philadelphia_police_emergencies\", engine, index=False)\n", "labels": {"reads": [{"table": "auto_show", "columns": null}], "writes": [{"table": "philadelphia_police_emergencies", "columns": ["worker_id", "total_points"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dw_users_full --columns arrival_time,mean_visibility_miles --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dw_users_full", "columns": ["arrival_time", "mean_visibility_miles"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table jobs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "jobs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ai_model > 367).all()\n# src table: freshwaterfinfish\nengine.execute(\"INSERT INTO drought_data SELECT * FROM freshwaterfinfish\")\n", "labels": {"reads": [{"table": "freshwaterfinfish", "columns": null}], "writes": [{"table": "drought_data", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO climate_adaptation (co2_emissions, crs_description) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "climate_adaptation", "columns": ["co2_emissions", "crs_description"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"experience\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"fair_wages\")\n", "labels": {"reads": [{"table": "experience", "columns": null}], "writes": [{"table": "fair_wages", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.ads_shipments_delta SELECT flag, farmname FROM marine_species_arctic_ocean WHERE flag > 386\"\n", "labels": {"reads": [{"table": "marine_species_arctic_ocean", "columns": ["flag", "farmname"]}], "writes": [{"table": "ads.ads_shipments_delta", "columns": ["flag", "farmname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_table(ctx, \"claims_processing_stages\")\nwrite_to_store(df, \"ship\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "claims_processing_stages", "columns": null}], "writes": [{"table": "ship", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table fleets --target-dir /tmp/land\n", "labels": {"reads": [{"table": "fleets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO exhibitionsartworks SELECT galleryname, pilot_name, distance FROM part_faults WHERE galleryname > 257\"\n", "labels": {"reads": [{"table": "part_faults", "columns": ["galleryname", "pilot_name", "distance"]}], "writes": [{"table": "exhibitionsartworks", "columns": ["galleryname", "pilot_name", "distance"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO clothing_brands SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"materials\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "materials", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg_users_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"donorprograms\")\n", "labels": {"reads": [{"table": "stg_users_daily", "columns": null}], "writes": [{"table": "donorprograms", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO membership SELECT * FROM legacy\nspark.sql(\"INSERT INTO view_product_availability SELECT device, waste_type, trial_id FROM regulatory_frameworks WHERE device > 408\")\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": ["device", "waste_type", "trial_id"]}], "writes": [{"table": "view_product_availability", "columns": ["device", "waste_type", "trial_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"review\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "review", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemicals_annual\").toPandas()\ndf[[\"invoice_id\", \"serviceid\"]].to_sql(\"contract_timeline\", engine, index=False)\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": null}], "writes": [{"table": "contract_timeline", "columns": ["invoice_id", "serviceid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO attendee_demographics (document_date, mineid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "attendee_demographics", "columns": ["document_date", "mineid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"researchers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "researchers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_products_full\").toPandas()\ndf[[\"creator\", \"decor\"]].to_sql(\"providers\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.stg_products_full", "columns": null}], "writes": [{"table": "providers", "columns": ["creator", "decor"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM store\", conn)\ndf.to_sql(\"claims_documents\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "store", "columns": null}], "writes": [{"table": "claims_documents", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_orders_full\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ads.ads_orders_full", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model clothing_brands depends on mart_orders_di\ndbt build --models clothing_brands --vars 'source: mart_orders_di'\n", "labels": {"reads": [{"table": "mart_orders_di", "columns": null}], "writes": [{"table": "clothing_brands", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"indie_artists\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "indie_artists", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"customer_month\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "customer_month", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ods.campaigns_di --columns studio,connection --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ods.campaigns_di", "columns": ["studio", "connection"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_salinity\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"state_budget\")\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": null}], "writes": [{"table": "state_budget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO num_employees SELECT time_minute, home_team_three_point, max_speed FROM patient_outcomes WHERE time_minute > 332\"\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": ["time_minute", "home_team_three_point", "max_speed"]}], "writes": [{"table": "num_employees", "columns": ["time_minute", "home_team_three_point", "max_speed"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart.mart_sessions_di --columns acidification_level,mine_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_sessions_di", "columns": ["acidification_level", "mine_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 381;\nSQL\n", "labels": {"reads": [{"table": "document_locations", "columns": ["usage", "emp_num"]}, {"table": "extraction_methods", "columns": ["tot_cred", "biome_id", "vendor", "spectators"]}], "writes": [{"table": "tree_types", "columns": ["tot_cred", "biome_id", "vendor", "spectators"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO shariah_compliant_loans SELECT tank, excavation_site_id FROM machines WHERE tank > 197\"\n", "labels": {"reads": [{"table": "machines", "columns": ["tank", "excavation_site_id"]}], "writes": [{"table": "shariah_compliant_loans", "columns": ["tank", "excavation_site_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.detention_type_code > 197).all()\n# src table: tech_accessibility_funding\nengine.execute(\"INSERT INTO platformstats SELECT * FROM tech_accessibility_funding\")\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": null}], "writes": [{"table": "platformstats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"appointment\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "appointment", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"countries\")\nexport_to_output(df, \"human_resources\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "countries", "columns": null}], "writes": [{"table": "human_resources", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ingredients (assistingnurse, is_recycled) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ingredients", "columns": ["assistingnurse", "is_recycled"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT birth_place, how_to_get_there FROM film LIMIT 342\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO team SELECT wildlife_type_id, art_movement, park_id, deliveryid FROM supplier_ethics WHERE wildlife_type_id > 393\")\n", "labels": {"reads": [{"table": "film", "columns": ["birth_place", "how_to_get_there"]}, {"table": "supplier_ethics", "columns": ["wildlife_type_id", "art_movement", "park_id", "deliveryid"]}], "writes": [{"table": "team", "columns": ["wildlife_type_id", "art_movement", "park_id", "deliveryid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.post_category > 128).all()\n# src table: green_certification\nengine.execute(\"INSERT INTO genre_songs SELECT * FROM green_certification\")\n", "labels": {"reads": [{"table": "green_certification", "columns": null}], "writes": [{"table": "genre_songs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"completed_training\").toPandas()\ndf[[\"virtual_tour_engagement_time\", \"report_date\"]].to_sql(\"ads_refunds_full\", engine, index=False)\n", "labels": {"reads": [{"table": "completed_training", "columns": null}], "writes": [{"table": "ads_refunds_full", "columns": ["virtual_tour_engagement_time", "report_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO state_budget SELECT visitor_country, offender_id FROM economic_diversification_projects WHERE visitor_country > 76\")\n", "labels": {"reads": [{"table": "economic_diversification_projects", "columns": ["visitor_country", "offender_id"]}], "writes": [{"table": "state_budget", "columns": ["visitor_country", "offender_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO school_enrollment SELECT sessionid, profits_billion, menu_item_id, wifi FROM ads.ads_refunds_hourly WHERE sessionid > 275\"], check=True)\n", "labels": {"reads": [{"table": "ads.ads_refunds_hourly", "columns": ["sessionid", "profits_billion", "menu_item_id", "wifi"]}], "writes": [{"table": "school_enrollment", "columns": ["sessionid", "profits_billion", "menu_item_id", "wifi"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO peacekeeping_units SELECT * FROM legacy\ncur.execute(\"SELECT ycard, actor_id FROM militarycyberops LIMIT 191\")\n", "labels": {"reads": [{"table": "militarycyberops", "columns": ["ycard", "actor_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO bi.device_log SELECT characteristic_name, patient_name FROM dw.dw_users_di WHERE characteristic_name > 488\")\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": ["characteristic_name", "patient_name"]}], "writes": [{"table": "bi.device_log", "columns": ["characteristic_name", "patient_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_shipments_full\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dispensary_sales\")\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": null}], "writes": [{"table": "dispensary_sales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"movie\");\ndf.write().mode(\"overwrite\").saveAsTable(\"wine\");\n", "labels": {"reads": [{"table": "movie", "columns": null}], "writes": [{"table": "wine", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO veterans SELECT * FROM legacy\ncur.execute(\"SELECT issue_count, dish_type FROM dwd.products_hourly LIMIT 88\")\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": ["issue_count", "dish_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"book\");\ndf.write().mode(\"overwrite\").saveAsTable(\"bustrips\");\n", "labels": {"reads": [{"table": "book", "columns": null}], "writes": [{"table": "bustrips", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM underwater_cables\"\n", "labels": {"reads": [{"table": "underwater_cables", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_ratings\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"farm_competition\")\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": null}], "writes": [{"table": "farm_competition", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model stg_users_daily depends on dw_vendors_di\ndbt run --models stg_users_daily --vars '{\"src\":\"dw_vendors_di\"}'\n", "labels": {"reads": [{"table": "dw_vendors_di", "columns": null}], "writes": [{"table": "stg_users_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"bi.events_df\")\nwrite_to_store(df, \"salinity_readings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.events_df", "columns": null}], "writes": [{"table": "salinity_readings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO phishing_targets (district, max_salary) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "phishing_targets", "columns": ["district", "max_salary"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO weapons (category, routename) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "weapons", "columns": ["category", "routename"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"shop\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"cases\")\n", "labels": {"reads": [{"table": "shop", "columns": null}], "writes": [{"table": "cases", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO shark_biomass SELECT bridgeid, operation, airport, purchase_date FROM bi.bi_events_full WHERE bridgeid > 458\");\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": ["bridgeid", "operation", "airport", "purchase_date"]}], "writes": [{"table": "shark_biomass", "columns": ["bridgeid", "operation", "airport", "purchase_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table stores --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"worker_union\").where(\"dt = current_date()\").writeTo(\"property_community\").append()\n", "labels": {"reads": [{"table": "worker_union", "columns": null}], "writes": [{"table": "property_community", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 139;\nEOF\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_df", "columns": ["programoutcomeid", "tech", "review_date", "test_result"]}], "writes": [{"table": "shop", "columns": ["programoutcomeid", "tech", "review_date", "test_result"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO communication_scores (milliseconds, trip_distance) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "communication_scores", "columns": ["milliseconds", "trip_distance"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart.device_log_hourly SELECT char_cells, chemical_id, community_members FROM shariah_financing WHERE char_cells > 74\"\n", "labels": {"reads": [{"table": "shariah_financing", "columns": ["char_cells", "chemical_id", "community_members"]}], "writes": [{"table": "mart.device_log_hourly", "columns": ["char_cells", "chemical_id", "community_members"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO producersnewmexico SELECT a.ingredient_id, b.customerid FROM field5 a JOIN dwd.dwd_orders_daily b ON a.employeeid = b.employeeid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "field5", "columns": null}, {"table": "dwd.dwd_orders_daily", "columns": null}], "writes": [{"table": "producersnewmexico", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"reservations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"deep_sea_expeditions\")\n", "labels": {"reads": [{"table": "reservations", "columns": null}], "writes": [{"table": "deep_sea_expeditions", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM cotton_source\"\n", "labels": {"reads": [{"table": "cotton_source", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO bi.bi_inventory SELECT vaccine_type, permits_issued, visit_month FROM makeup_sales WHERE vaccine_type > 260\");\n", "labels": {"reads": [{"table": "makeup_sales", "columns": ["vaccine_type", "permits_issued", "visit_month"]}], "writes": [{"table": "bi.bi_inventory", "columns": ["vaccine_type", "permits_issued", "visit_month"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO cyber_incidents SELECT * FROM legacy\ncur.execute(\"SELECT vehicleid, restypename FROM online_platform LIMIT 343\")\n", "labels": {"reads": [{"table": "online_platform", "columns": ["vehicleid", "restypename"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO film_category SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"criminalcases\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "criminalcases", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO recyclers SELECT a.device, b.catalog_publisher FROM veterans a JOIN southchinasea.wells b ON a.loadingstart = b.loadingstart\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "veterans", "columns": null}, {"table": "southchinasea.wells", "columns": null}], "writes": [{"table": "recyclers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT state_county, communitytype FROM runs LIMIT 132\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "runs", "columns": ["state_county", "communitytype"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table average --target-dir /tmp/land\n", "labels": {"reads": [{"table": "average", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.measurement_date > 202).all()\n# src table: sustainability_fact\nengine.execute(\"INSERT INTO virtual_tour_revenue SELECT * FROM sustainability_fact\")\n", "labels": {"reads": [{"table": "sustainability_fact", "columns": null}], "writes": [{"table": "virtual_tour_revenue", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 218;\nEOF\n", "labels": {"reads": [{"table": "firestations", "columns": ["co2_reduction_tons", "premise_details", "seal_species", "claim_status_description"]}], "writes": [{"table": "expensive_space_missions", "columns": ["co2_reduction_tons", "premise_details", "seal_species", "claim_status_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO cultural_heritage SELECT staff_id, family_name, energy_id FROM ods.ods_coupon_use_di WHERE staff_id > 98\"\n", "labels": {"reads": [{"table": "ods.ods_coupon_use_di", "columns": ["staff_id", "family_name", "energy_id"]}], "writes": [{"table": "cultural_heritage", "columns": ["staff_id", "family_name", "energy_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_input(ctx, \"drills\")\npersist_to_target(df, \"dallas_fire_incidents\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "drills", "columns": null}], "writes": [{"table": "dallas_fire_incidents", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO production_data SELECT instructor, donation_amount FROM stg.events_hourly WHERE instructor > 393\")\n", "labels": {"reads": [{"table": "stg.events_hourly", "columns": ["instructor", "donation_amount"]}], "writes": [{"table": "production_data", "columns": ["instructor", "donation_amount"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bridges SELECT claim_amount, contact_number FROM bi_shipments_daily WHERE claim_amount > 129\"\n", "labels": {"reads": [{"table": "bi_shipments_daily", "columns": ["claim_amount", "contact_number"]}], "writes": [{"table": "bridges", "columns": ["claim_amount", "contact_number"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table intelligenceoperations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "intelligenceoperations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"athlete_stats\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "athlete_stats", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO projecttimeline SELECT fueldate, cust_name, mealname, bias_score FROM dw.dw_member_point_hourly WHERE fueldate > 92\")\n", "labels": {"reads": [{"table": "dw.dw_member_point_hourly", "columns": ["fueldate", "cust_name", "mealname", "bias_score"]}], "writes": [{"table": "projecttimeline", "columns": ["fueldate", "cust_name", "mealname", "bias_score"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"grapes\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"mart.clicks\")\n", "labels": {"reads": [{"table": "grapes", "columns": null}], "writes": [{"table": "mart.clicks", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO urban_farms SELECT system_type, access_date FROM basketball_teams WHERE system_type > 77\")\n", "labels": {"reads": [{"table": "basketball_teams", "columns": ["system_type", "access_date"]}], "writes": [{"table": "urban_farms", "columns": ["system_type", "access_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"round\")\npush_to_sink(df, \"dw_risk_score_daily\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "round", "columns": null}], "writes": [{"table": "dw_risk_score_daily", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"circular_supply_chain_products\").toPandas()\ndf[[\"goal_date\", \"awayteamid\"]].to_sql(\"mart_orders_di\", engine, index=False)\n", "labels": {"reads": [{"table": "circular_supply_chain_products", "columns": null}], "writes": [{"table": "mart_orders_di", "columns": ["goal_date", "awayteamid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO wellbeing_programs SELECT sustainability_rating, document_status_description, follows_ethical_practices FROM west_providers WHERE sustainability_rating > 18\")\n", "labels": {"reads": [{"table": "west_providers", "columns": ["sustainability_rating", "document_status_description", "follows_ethical_practices"]}], "writes": [{"table": "wellbeing_programs", "columns": ["sustainability_rating", "document_status_description", "follows_ethical_practices"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM smartcitycosts\"\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model peakhours depends on channel\ndbt build --models peakhours --vars '{\"src\":\"channel\"}'\n", "labels": {"reads": [{"table": "channel", "columns": null}], "writes": [{"table": "peakhours", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"species_observations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "species_observations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.clicks_delta\", conn)\ndf.to_sql(\"ads.orders\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.clicks_delta", "columns": null}], "writes": [{"table": "ads.orders", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO life_expectancy SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"warehouses\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "warehouses", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT art_movement, vendorid FROM climate_finance LIMIT 16\")\nif not rows:\n logger.warning('empty result')\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO sales_quarterly SELECT lifespan, sale_date, property_id, beds FROM countryintelligenceops WHERE lifespan > 391\")\n", "labels": {"reads": [{"table": "climate_finance", "columns": ["art_movement", "vendorid"]}, {"table": "countryintelligenceops", "columns": ["lifespan", "sale_date", "property_id", "beds"]}], "writes": [{"table": "sales_quarterly", "columns": ["lifespan", "sale_date", "property_id", "beds"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dorm SELECT insurancetype, mission_id, treatment_id, ad_id FROM arrivals WHERE insurancetype > 344\"], check=True)\n", "labels": {"reads": [{"table": "arrivals", "columns": ["insurancetype", "mission_id", "treatment_id", "ad_id"]}], "writes": [{"table": "dorm", "columns": ["insurancetype", "mission_id", "treatment_id", "ad_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model crime_incidents depends on organic_farms\ndbt build -s crime_incidents --vars 'source: organic_farms'\n", "labels": {"reads": [{"table": "organic_farms", "columns": null}], "writes": [{"table": "crime_incidents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"whale_sharks\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "whale_sharks", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"safety_data\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"agricultural_projects\")\n", "labels": {"reads": [{"table": "safety_data", "columns": null}], "writes": [{"table": "agricultural_projects", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO stg.stg_exposure_di SELECT founders_lgbtq, incidents, issues FROM dwd.coupon_use_full WHERE founders_lgbtq > 150\")\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": ["founders_lgbtq", "incidents", "issues"]}], "writes": [{"table": "stg.stg_exposure_di", "columns": ["founders_lgbtq", "incidents", "issues"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO autoshow SELECT a.plant, b.waste_generation FROM financial_capability_program a JOIN climate_communication b ON a.volume_id = b.volume_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "financial_capability_program", "columns": null}, {"table": "climate_communication", "columns": null}], "writes": [{"table": "autoshow", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"platformg\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"production_yearly\")\n", "labels": {"reads": [{"table": "platformg", "columns": null}], "writes": [{"table": "production_yearly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table opendatainitiatives --columns fish_population,attribute_data_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "opendatainitiatives", "columns": ["fish_population", "attribute_data_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.bi_events_df SELECT spacecraft_name, playername FROM cyber_incidents WHERE spacecraft_name > 456\"\n", "labels": {"reads": [{"table": "cyber_incidents", "columns": ["spacecraft_name", "playername"]}], "writes": [{"table": "bi.bi_events_df", "columns": ["spacecraft_name", "playername"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO participants_in_events SELECT value, asset_id, median_home_value FROM tb_cases WHERE value > 36\")\n", "labels": {"reads": [{"table": "tb_cases", "columns": ["value", "asset_id", "median_home_value"]}], "writes": [{"table": "participants_in_events", "columns": ["value", "asset_id", "median_home_value"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 112;\nEOF\n", "labels": {"reads": [{"table": "tunnels", "columns": ["number_of_observations", "trial_year"]}], "writes": [{"table": "renewable_projects", "columns": ["number_of_observations", "trial_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO algorithmic_fairness SELECT restaurantname, violation_id FROM government.region WHERE restaurantname > 17\"\n", "labels": {"reads": [{"table": "government.region", "columns": ["restaurantname", "violation_id"]}], "writes": [{"table": "algorithmic_fairness", "columns": ["restaurantname", "violation_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM fireincidents\"\n", "labels": {"reads": [{"table": "fireincidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO lots SELECT quality_rank, stateid, account_balance, formats FROM coowners WHERE quality_rank > 447\")\n", "labels": {"reads": [{"table": "coowners", "columns": ["quality_rank", "stateid", "account_balance", "formats"]}], "writes": [{"table": "lots", "columns": ["quality_rank", "stateid", "account_balance", "formats"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ai_safety_papers2 SELECT * FROM legacy\ncur.execute(\"SELECT dept_code, role_description FROM solar_farms LIMIT 242\")\n", "labels": {"reads": [{"table": "solar_farms", "columns": ["dept_code", "role_description"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT technology, member FROM energy_production\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"assessment_notes\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "energy_production", "columns": ["technology", "member"]}], "writes": [{"table": "assessment_notes", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO grant (shariah_compliant_investment_amount, bname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "grant", "columns": ["shariah_compliant_investment_amount", "bname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table training_programs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "training_programs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO trainingprograms SELECT approach, coupon_id, researcher_name, stayid FROM military_contracts WHERE approach > 171\"\n", "labels": {"reads": [{"table": "military_contracts", "columns": ["approach", "coupon_id", "researcher_name", "stayid"]}], "writes": [{"table": "trainingprograms", "columns": ["approach", "coupon_id", "researcher_name", "stayid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO team_franchise SELECT * FROM legacy\ncur.execute(\"SELECT building_address, team FROM vaccinations LIMIT 283\")\n", "labels": {"reads": [{"table": "vaccinations", "columns": ["building_address", "team"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 147;\nSQL\n", "labels": {"reads": [{"table": "participants", "columns": ["workoutdate", "storename"]}, {"table": "australia_offset_programs", "columns": ["staff_first_name", "lettergrade", "address_type_code"]}], "writes": [{"table": "mart.clicks_delta", "columns": ["staff_first_name", "lettergrade", "address_type_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.genrename > 117).all()\n# src table: posts\nengine.execute(\"INSERT INTO biodiversity SELECT * FROM posts\")\n", "labels": {"reads": [{"table": "posts", "columns": null}], "writes": [{"table": "biodiversity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 266;\nSQL\n", "labels": {"reads": [{"table": "mart.device_log_hourly", "columns": ["sale_year", "agreementid"]}, {"table": "colorado_river_basin", "columns": ["tour_id", "bias_score"]}], "writes": [{"table": "bi.clicks_hourly", "columns": ["tour_id", "bias_score"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO latam_schema.education_budget SELECT * FROM legacy\nspark.sql(\"INSERT INTO wastewatertreatment SELECT installation_year, document_type_description, county_id FROM navalvessels WHERE installation_year > 461\")\n", "labels": {"reads": [{"table": "navalvessels", "columns": ["installation_year", "document_type_description", "county_id"]}], "writes": [{"table": "wastewatertreatment", "columns": ["installation_year", "document_type_description", "county_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO classroom SELECT lot_id, num_beds FROM spacecraftmanufacturing WHERE lot_id > 275\")\n", "labels": {"reads": [{"table": "spacecraftmanufacturing", "columns": ["lot_id", "num_beds"]}], "writes": [{"table": "classroom", "columns": ["lot_id", "num_beds"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ods.campaigns_di SELECT check_in_id, orderid FROM distributors WHERE check_in_id > 140\"\n", "labels": {"reads": [{"table": "distributors", "columns": ["check_in_id", "orderid"]}], "writes": [{"table": "ods.campaigns_di", "columns": ["check_in_id", "orderid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"militaryoperations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "militaryoperations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO sustainable_urban_properties_2 SELECT location_text, attorney_last_name, shippingmethod, potency FROM bi.member_point_full WHERE location_text > 305\"\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": ["location_text", "attorney_last_name", "shippingmethod", "potency"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["location_text", "attorney_last_name", "shippingmethod", "potency"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO faculty SELECT startdate, customer_number FROM co2_sequestration WHERE startdate > 62\"\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": ["startdate", "customer_number"]}], "writes": [{"table": "faculty", "columns": ["startdate", "customer_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_orders_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw.dw_orders_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"chip_model\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"canada_tech\")\n", "labels": {"reads": [{"table": "chip_model", "columns": null}], "writes": [{"table": "canada_tech", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT meter_100, station_name FROM behavior_incident\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"green_building_projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "behavior_incident", "columns": ["meter_100", "station_name"]}], "writes": [{"table": "green_building_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO appointment SELECT bicycle_id, num_virtual_tours, totalamount FROM ref_shipping_agents WHERE bicycle_id > 132\"], check=True)\n", "labels": {"reads": [{"table": "ref_shipping_agents", "columns": ["bicycle_id", "num_virtual_tours", "totalamount"]}], "writes": [{"table": "appointment", "columns": ["bicycle_id", "num_virtual_tours", "totalamount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"customer_address_history\")\nsrc.write.insertInto(\"checking\", overwrite=True)\n", "labels": {"reads": [{"table": "customer_address_history", "columns": null}], "writes": [{"table": "checking", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.recruiterid > 28).all()\n# src table: membership_data\nengine.execute(\"INSERT INTO stg.stg_events_hourly SELECT * FROM membership_data\")\n", "labels": {"reads": [{"table": "membership_data", "columns": null}], "writes": [{"table": "stg.stg_events_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"team_revenue\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "team_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO biotech_startups (destination_state, portname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "biotech_startups", "columns": ["destination_state", "portname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"journal\").toPandas()\ndf[[\"visitor_name\", \"co2_offset_amount\"]].to_sql(\"virtual_tour_engagement\", engine, index=False)\n", "labels": {"reads": [{"table": "journal", "columns": null}], "writes": [{"table": "virtual_tour_engagement", "columns": ["visitor_name", "co2_offset_amount"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO skincareinventory SELECT * FROM legacy\nspark.sql(\"INSERT INTO dws.dws_users_hourly SELECT cost_id, waste_type, booking_date, college FROM shoes WHERE cost_id > 242\")\n", "labels": {"reads": [{"table": "shoes", "columns": ["cost_id", "waste_type", "booking_date", "college"]}], "writes": [{"table": "dws.dws_users_hourly", "columns": ["cost_id", "waste_type", "booking_date", "college"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bridge\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bridge", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO leed_buildings SELECT horizontal_bar_points, temperature FROM timber_sales WHERE horizontal_bar_points > 412\"\n", "labels": {"reads": [{"table": "timber_sales", "columns": ["horizontal_bar_points", "temperature"]}], "writes": [{"table": "leed_buildings", "columns": ["horizontal_bar_points", "temperature"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO midwest_region SELECT a.category_id, b.supplier_name FROM mart.mart_users_di a JOIN product_catalog b ON a.is_vegetarian = b.is_vegetarian\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": null}, {"table": "product_catalog", "columns": null}], "writes": [{"table": "midwest_region", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM performance_scores\", conn)\ndf.to_sql(\"bi.bi_inventory_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "performance_scores", "columns": null}], "writes": [{"table": "bi.bi_inventory_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"obesity\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "obesity", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO green_certification SELECT shares, date_in_location_from, time_of_day FROM invoice_lines WHERE shares > 352\"], check=True)\n", "labels": {"reads": [{"table": "invoice_lines", "columns": ["shares", "date_in_location_from", "time_of_day"]}], "writes": [{"table": "green_certification", "columns": ["shares", "date_in_location_from", "time_of_day"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 290;\nEOF\n", "labels": {"reads": [{"table": "pediatricians", "columns": ["patientid", "to_address", "workerid", "num_volunteers"]}], "writes": [{"table": "educators", "columns": ["patientid", "to_address", "workerid", "num_volunteers"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 486;\nEOF\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": ["review_date", "participant_type_code", "mouse_id"]}], "writes": [{"table": "redundant_billing_data", "columns": ["review_date", "participant_type_code", "mouse_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO student_course_attendance SELECT tour_name, socialimpactscore, threats FROM city_tech WHERE tour_name > 130\")\n", "labels": {"reads": [{"table": "city_tech", "columns": ["tour_name", "socialimpactscore", "threats"]}], "writes": [{"table": "student_course_attendance", "columns": ["tour_name", "socialimpactscore", "threats"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT credits, sent_date FROM disaster_response LIMIT 205\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO wearable_metrics SELECT isfirstattendee, support_rep_id, recorded_by_staff_id, license_type FROM food_safety_inspections WHERE isfirstattendee > 346\")\n", "labels": {"reads": [{"table": "disaster_response", "columns": ["credits", "sent_date"]}, {"table": "food_safety_inspections", "columns": ["isfirstattendee", "support_rep_id", "recorded_by_staff_id", "license_type"]}], "writes": [{"table": "wearable_metrics", "columns": ["isfirstattendee", "support_rep_id", "recorded_by_staff_id", "license_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.risk_score_df\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.refunds SELECT practices, domestic_passengers, sensor_id FROM parties WHERE practices > 362\"\n", "labels": {"reads": [{"table": "parties", "columns": ["practices", "domestic_passengers", "sensor_id"]}], "writes": [{"table": "stg.refunds", "columns": ["practices", "domestic_passengers", "sensor_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT workoutdate, installation_date FROM dws_coupon_use_df LIMIT 303\")\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO discount_coupons SELECT clubdesc, goldquantity, donationid FROM stg.stg_events_hourly WHERE clubdesc > 491\")\n", "labels": {"reads": [{"table": "dws_coupon_use_df", "columns": ["workoutdate", "installation_date"]}, {"table": "stg.stg_events_hourly", "columns": ["clubdesc", "goldquantity", "donationid"]}], "writes": [{"table": "discount_coupons", "columns": ["clubdesc", "goldquantity", "donationid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sustainablebrands SELECT member_id, dissolved_oxygen, manufacturer, satelliteid FROM vessel_capacity WHERE member_id > 471\"\n", "labels": {"reads": [{"table": "vessel_capacity", "columns": ["member_id", "dissolved_oxygen", "manufacturer", "satelliteid"]}], "writes": [{"table": "sustainablebrands", "columns": ["member_id", "dissolved_oxygen", "manufacturer", "satelliteid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO tokyo_motor_show SELECT precipitation, granteeid, emergency_type, affirmative FROM manufacturingplants WHERE precipitation > 155\")\n", "labels": {"reads": [{"table": "manufacturingplants", "columns": ["precipitation", "granteeid", "emergency_type", "affirmative"]}], "writes": [{"table": "tokyo_motor_show", "columns": ["precipitation", "granteeid", "emergency_type", "affirmative"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table shipments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "shipments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mining.company\").toPandas()\ndf[[\"equipment_name\", \"healthequitymetricscore\"]].to_sql(\"habitat\", engine, index=False)\n", "labels": {"reads": [{"table": "mining.company", "columns": null}], "writes": [{"table": "habitat", "columns": ["equipment_name", "healthequitymetricscore"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO rural_infrastructure SELECT a.units_owned, b.tv_show_id FROM emerging_markets.digital_assets a JOIN mart.mart_events_di b ON a.client_name = b.client_name\"\n", "labels": {"reads": [{"table": "emerging_markets.digital_assets", "columns": null}, {"table": "mart.mart_events_di", "columns": null}], "writes": [{"table": "rural_infrastructure", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi.users_full\");\ndf.write().mode(\"overwrite\").saveAsTable(\"lots\");\n", "labels": {"reads": [{"table": "bi.users_full", "columns": null}], "writes": [{"table": "lots", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO africa_schema.african_mines SELECT material_type, emp_lname, sales_transaction_id FROM production WHERE material_type > 445\"], check=True)\n", "labels": {"reads": [{"table": "production", "columns": ["material_type", "emp_lname", "sales_transaction_id"]}], "writes": [{"table": "africa_schema.african_mines", "columns": ["material_type", "emp_lname", "sales_transaction_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table gamesales --target-dir /tmp/land\n", "labels": {"reads": [{"table": "gamesales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO facility_production SELECT stationid, total_amount_purchased FROM esportsteamsafrica WHERE stationid > 114\"], check=True)\n", "labels": {"reads": [{"table": "esportsteamsafrica", "columns": ["stationid", "total_amount_purchased"]}], "writes": [{"table": "facility_production", "columns": ["stationid", "total_amount_purchased"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO cerium_production SELECT * FROM legacy\ncur.execute(\"SELECT velocity, flightid FROM exhibitionsartworks LIMIT 337\")\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": ["velocity", "flightid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 164;\nEOF\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": ["platform", "team_name", "sustainability_score", "problem_description"]}], "writes": [{"table": "maintenance", "columns": ["platform", "team_name", "sustainability_score", "problem_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO communities SELECT operationid, attendance, account_id, ride_id FROM donations_insert_2 WHERE operationid > 262\")\n", "labels": {"reads": [{"table": "donations_insert_2", "columns": ["operationid", "attendance", "account_id", "ride_id"]}], "writes": [{"table": "communities", "columns": ["operationid", "attendance", "account_id", "ride_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donationyear, property_price FROM economic_diversification_efforts LIMIT 171\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": ["donationyear", "property_price"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"patient_satisfaction\")\nsrc.write.insertInto(\"student_tests_taken\", overwrite=True)\n", "labels": {"reads": [{"table": "patient_satisfaction", "columns": null}], "writes": [{"table": "student_tests_taken", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ai_adoption, data_usage FROM global_tournament LIMIT 287\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "global_tournament", "columns": ["ai_adoption", "data_usage"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"broadband_customers_global\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO employeepromotions SELECT culturalcompetency, official_name, city_name, event_date FROM carbon_offsets WHERE culturalcompetency > 7\"], check=True)\n", "labels": {"reads": [{"table": "carbon_offsets", "columns": ["culturalcompetency", "official_name", "city_name", "event_date"]}], "writes": [{"table": "employeepromotions", "columns": ["culturalcompetency", "official_name", "city_name", "event_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"teacher_development_race\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"chemicalproducts\")\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": null}], "writes": [{"table": "chemicalproducts", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bioprocess_engineering SELECT artworkyear, numpieces, dormid FROM instructor WHERE artworkyear > 364\"], check=True)\n", "labels": {"reads": [{"table": "instructor", "columns": ["artworkyear", "numpieces", "dormid"]}], "writes": [{"table": "bioprocess_engineering", "columns": ["artworkyear", "numpieces", "dormid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"algorithmic_fairness_incidents\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"cosmetics.lipstick_spf_data\")\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents", "columns": null}], "writes": [{"table": "cosmetics.lipstick_spf_data", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainability_metrics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"hydro_power\")\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": null}], "writes": [{"table": "hydro_power", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dw.events_hourly\");\ndf.write().mode(\"overwrite\").saveAsTable(\"urban_initiatives\");\n", "labels": {"reads": [{"table": "dw.events_hourly", "columns": null}], "writes": [{"table": "urban_initiatives", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.mart_coupon_use_delta\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO characteristics SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO gene SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO results SELECT attribute_value, origin FROM playergamehistory WHERE attribute_value > 274\")\n", "labels": {"reads": [{"table": "playergamehistory", "columns": ["attribute_value", "origin"]}], "writes": [{"table": "results", "columns": ["attribute_value", "origin"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO follows SELECT * FROM legacy\ncur.execute(\"SELECT forename, undergraduate FROM mart.mart_coupon_use_full LIMIT 57\")\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_full", "columns": ["forename", "undergraduate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table request --target-dir /tmp/land\n", "labels": {"reads": [{"table": "request", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"military_personnel_africa\")\n", "labels": {"reads": [{"table": "rural_projects", "columns": null}], "writes": [{"table": "military_personnel_africa", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO maintenance_contracts SELECT num_of_component, date_valid_to, production_cost FROM community_development_projects WHERE num_of_component > 134\"], check=True)\n", "labels": {"reads": [{"table": "community_development_projects", "columns": ["num_of_component", "date_valid_to", "production_cost"]}], "writes": [{"table": "maintenance_contracts", "columns": ["num_of_component", "date_valid_to", "production_cost"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"spacecraft\")\nsrc.write.insertInto(\"textileworkers\", overwrite=True)\n", "labels": {"reads": [{"table": "spacecraft", "columns": null}], "writes": [{"table": "textileworkers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"strategies\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "strategies", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"event\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "event", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM nba\"\n", "labels": {"reads": [{"table": "nba", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO medicine_enzyme_interaction SELECT vin, incident_date, firstdonationdate FROM fans_merchandise_basketball WHERE vin > 105\"\n", "labels": {"reads": [{"table": "fans_merchandise_basketball", "columns": ["vin", "incident_date", "firstdonationdate"]}], "writes": [{"table": "medicine_enzyme_interaction", "columns": ["vin", "incident_date", "firstdonationdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart_campaigns_delta --columns count,average_age --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart_campaigns_delta", "columns": ["count", "average_age"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO faculty SELECT rec_engine, member_id, gametype, session_language FROM mars_spacecraft WHERE rec_engine > 89\"], check=True)\n", "labels": {"reads": [{"table": "mars_spacecraft", "columns": ["rec_engine", "member_id", "gametype", "session_language"]}], "writes": [{"table": "faculty", "columns": ["rec_engine", "member_id", "gametype", "session_language"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donation_amount, date_of_latest_revision FROM contract_negotiations_un LIMIT 449\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "contract_negotiations_un", "columns": ["donation_amount", "date_of_latest_revision"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"research_grants\")\nsink_to_store(df, \"ocean_acidity\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "research_grants", "columns": null}], "writes": [{"table": "ocean_acidity", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.member_point_full SELECT a.factory_name, b.booking_start_date FROM sustainable_urban a JOIN sustainable_urban_properties_2 b ON a.order_quantity = b.order_quantity\"\n", "labels": {"reads": [{"table": "sustainable_urban", "columns": null}, {"table": "sustainable_urban_properties_2", "columns": null}], "writes": [{"table": "bi.member_point_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"erc20_transactions\").where(\"dt = current_date()\").writeTo(\"dwd.dwd_risk_score_delta\").append()\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": null}], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT inspection_time, num_songs FROM item_prices LIMIT 233\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "item_prices", "columns": ["inspection_time", "num_songs"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"electoral_register\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.mart_device_log_hourly\")\n", "labels": {"reads": [{"table": "electoral_register", "columns": null}], "writes": [{"table": "mart.mart_device_log_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"public_schools\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.campaigns_df\")\n", "labels": {"reads": [{"table": "public_schools", "columns": null}], "writes": [{"table": "stg.campaigns_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO equipment_maintenance SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"candidate_assessments\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"carbon_offsets\")\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": null}], "writes": [{"table": "carbon_offsets", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"workplaces\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"colorado_river_basin\")\n", "labels": {"reads": [{"table": "workplaces", "columns": null}], "writes": [{"table": "colorado_river_basin", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO traffic_citations SELECT * FROM legacy\ncur.execute(\"SELECT num_tools, member FROM directors LIMIT 358\")\n", "labels": {"reads": [{"table": "directors", "columns": ["num_tools", "member"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"drug_approval\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "drug_approval", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO mart_orders_di SELECT startup_id, lat FROM carbon_offset_south_america WHERE startup_id > 370\")\n", "labels": {"reads": [{"table": "carbon_offset_south_america", "columns": ["startup_id", "lat"]}], "writes": [{"table": "mart_orders_di", "columns": ["startup_id", "lat"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO maintenance_schedule SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table artwork_styles --columns do_value,categoryid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "artwork_styles", "columns": ["do_value", "categoryid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads_users_hourly\").toPandas()\ndf[[\"hospital_id\", \"user_name\"]].to_sql(\"mart.vendors_full\", engine, index=False)\n", "labels": {"reads": [{"table": "ads_users_hourly", "columns": null}], "writes": [{"table": "mart.vendors_full", "columns": ["hospital_id", "user_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO tunnels (region_id, wellid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "tunnels", "columns": ["region_id", "wellid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.claimtype > 120).all()\n# src table: product\nengine.execute(\"INSERT INTO maintenance_contracts SELECT * FROM product\")\n", "labels": {"reads": [{"table": "product", "columns": null}], "writes": [{"table": "maintenance_contracts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"genre\").toPandas()\ndf[[\"employmentdate\", \"volume\"]].to_sql(\"beverages\", engine, index=False)\n", "labels": {"reads": [{"table": "genre", "columns": null}], "writes": [{"table": "beverages", "columns": ["employmentdate", "volume"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO socially_responsible_lending SELECT outcome_description, other_account_details, supplier_company_id, ticket_id FROM gamestats WHERE outcome_description > 362\"\n", "labels": {"reads": [{"table": "gamestats", "columns": ["outcome_description", "other_account_details", "supplier_company_id", "ticket_id"]}], "writes": [{"table": "socially_responsible_lending", "columns": ["outcome_description", "other_account_details", "supplier_company_id", "ticket_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"sponsor_trials\").where(\"dt = current_date()\").writeTo(\"artwork\").append()\n", "labels": {"reads": [{"table": "sponsor_trials", "columns": null}], "writes": [{"table": "artwork", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"member_data\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "member_data", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.thing_id > 280).all()\n# src table: dailystreams\nengine.execute(\"INSERT INTO bus_routes SELECT * FROM dailystreams\")\n", "labels": {"reads": [{"table": "dailystreams", "columns": null}], "writes": [{"table": "bus_routes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO paintings (q1_2022_views, sale_price) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "paintings", "columns": ["q1_2022_views", "sale_price"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"drilling_rigs\")\nupsert_to_sink(df, \"circular_economy_initiatives\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "drilling_rigs", "columns": null}], "writes": [{"table": "circular_economy_initiatives", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO australian_states SELECT zip_postcode, depth, truck_licence_number FROM mart_refunds WHERE zip_postcode > 70\")\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["zip_postcode", "depth", "truck_licence_number"]}], "writes": [{"table": "australian_states", "columns": ["zip_postcode", "depth", "truck_licence_number"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"community_events\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws.dws_cart_item_daily\")\n", "labels": {"reads": [{"table": "community_events", "columns": null}], "writes": [{"table": "dws.dws_cart_item_daily", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ship_agent SELECT program_name, effort FROM milestones WHERE program_name > 16\"\n", "labels": {"reads": [{"table": "milestones", "columns": ["program_name", "effort"]}], "writes": [{"table": "ship_agent", "columns": ["program_name", "effort"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"eco_materials\")\nwrite_to_store(df, \"course_authors_and_tutors\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "eco_materials", "columns": null}], "writes": [{"table": "course_authors_and_tutors", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT investment_date, investment_id FROM veteran_occupations LIMIT 451\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO indie_artists SELECT graphics_mode, contract_start, grantid, transportation_method FROM salinity_readings WHERE graphics_mode > 423\")\n", "labels": {"reads": [{"table": "veteran_occupations", "columns": ["investment_date", "investment_id"]}, {"table": "salinity_readings", "columns": ["graphics_mode", "contract_start", "grantid", "transportation_method"]}], "writes": [{"table": "indie_artists", "columns": ["graphics_mode", "contract_start", "grantid", "transportation_method"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"rd_expenditure\")\nsrc.write.insertInto(\"art\", overwrite=True)\n", "labels": {"reads": [{"table": "rd_expenditure", "columns": null}], "writes": [{"table": "art", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO autonomousvehicleaccidents SELECT * FROM legacy\nspark.sql(\"INSERT INTO waterconservationbudget SELECT s_id, participant, permitid FROM vesselfuel WHERE s_id > 254\")\n", "labels": {"reads": [{"table": "vesselfuel", "columns": ["s_id", "participant", "permitid"]}], "writes": [{"table": "waterconservationbudget", "columns": ["s_id", "participant", "permitid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"textileworkers\")\ndump_to_store(df, \"ocean_health_monitor\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "textileworkers", "columns": null}], "writes": [{"table": "ocean_health_monitor", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO investment_accounts SELECT guest_first_name, facid, dishid, added_date FROM student_tests_taken WHERE guest_first_name > 207\"\n", "labels": {"reads": [{"table": "student_tests_taken", "columns": ["guest_first_name", "facid", "dishid", "added_date"]}], "writes": [{"table": "investment_accounts", "columns": ["guest_first_name", "facid", "dishid", "added_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO machine_emissions SELECT prominence, health_equity_metric_2, stu_gpa FROM laptimes WHERE prominence > 323\")\n", "labels": {"reads": [{"table": "laptimes", "columns": ["prominence", "health_equity_metric_2", "stu_gpa"]}], "writes": [{"table": "machine_emissions", "columns": ["prominence", "health_equity_metric_2", "stu_gpa"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO auctions SELECT applicant, water_type, shipping_agent_name FROM carbon_offset_programs WHERE applicant > 338\")\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": ["applicant", "water_type", "shipping_agent_name"]}], "writes": [{"table": "auctions", "columns": ["applicant", "water_type", "shipping_agent_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO languagesatrisk SELECT publicationid, pass_fail, donationid FROM document_types WHERE publicationid > 328\"\n", "labels": {"reads": [{"table": "document_types", "columns": ["publicationid", "pass_fail", "donationid"]}], "writes": [{"table": "languagesatrisk", "columns": ["publicationid", "pass_fail", "donationid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT item_price, virtual_tour_engagement_time FROM stg.stg_users LIMIT 27\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "stg.stg_users", "columns": ["item_price", "virtual_tour_engagement_time"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model communityengagements depends on ai_projects\ndbt run --models communityengagements --vars '{\"source_table\":\"ai_projects\"}'\n", "labels": {"reads": [{"table": "ai_projects", "columns": null}], "writes": [{"table": "communityengagements", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"watertreatmentplants\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "watertreatmentplants", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"royal_family\").where(\"dt = current_date()\").writeTo(\"public.police_calls\").append()\n", "labels": {"reads": [{"table": "royal_family", "columns": null}], "writes": [{"table": "public.police_calls", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO gamedata SELECT certified, shop_details FROM social_good_education WHERE certified > 128\");\n", "labels": {"reads": [{"table": "social_good_education", "columns": ["certified", "shop_details"]}], "writes": [{"table": "gamedata", "columns": ["certified", "shop_details"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM financial_capability_programs\", conn)\ndf.to_sql(\"flights\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "financial_capability_programs", "columns": null}], "writes": [{"table": "flights", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"preferences\")\nwrite_to_store(df, \"security_incidents\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "preferences", "columns": null}], "writes": [{"table": "security_incidents", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ucl_top10\"\n", "labels": {"reads": [{"table": "ucl_top10", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"disabilityadvocacy\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"investment\")\n", "labels": {"reads": [{"table": "disabilityadvocacy", "columns": null}], "writes": [{"table": "investment", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO claims_documents SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"product\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "product", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 123;\nSQL\n", "labels": {"reads": [{"table": "securityincidents", "columns": ["financial_capability_score", "area_id"]}, {"table": "match_season", "columns": ["is_cruelty_free", "savingsid", "tourist_attraction_id"]}], "writes": [{"table": "customer_month", "columns": ["is_cruelty_free", "savingsid", "tourist_attraction_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 143;\nSQL\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": ["passenger_count", "max_page_size"]}, {"table": "bi_shipments_daily", "columns": ["dphone", "exit_type"]}], "writes": [{"table": "defense_diplomacy", "columns": ["dphone", "exit_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO elements_price SELECT organisation_type_description, image_url, ota_name, routeid FROM regulatory_frameworks WHERE organisation_type_description > 199\"\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": ["organisation_type_description", "image_url", "ota_name", "routeid"]}], "writes": [{"table": "elements_price", "columns": ["organisation_type_description", "image_url", "ota_name", "routeid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO bi.bi_member_point SELECT meal_name, memory_in_g, num_schools, stuid FROM landfill_capacity_north_america WHERE meal_name > 90\")\n", "labels": {"reads": [{"table": "landfill_capacity_north_america", "columns": ["meal_name", "memory_in_g", "num_schools", "stuid"]}], "writes": [{"table": "bi.bi_member_point", "columns": ["meal_name", "memory_in_g", "num_schools", "stuid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"smart_cities\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bioreactor\")\n", "labels": {"reads": [{"table": "smart_cities", "columns": null}], "writes": [{"table": "bioreactor", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO salesdata SELECT * FROM legacy\nspark.sql(\"INSERT INTO al_jazeera_data SELECT state_name, rooms, day_number, election_cycle FROM jupiter_spacecraft WHERE state_name > 483\")\n", "labels": {"reads": [{"table": "jupiter_spacecraft", "columns": ["state_name", "rooms", "day_number", "election_cycle"]}], "writes": [{"table": "al_jazeera_data", "columns": ["state_name", "rooms", "day_number", "election_cycle"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"skincareproducts\").toPandas()\ndf[[\"mission_category\", \"access_date\"]].to_sql(\"educators\", engine, index=False)\n", "labels": {"reads": [{"table": "skincareproducts", "columns": null}], "writes": [{"table": "educators", "columns": ["mission_category", "access_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 175;\nEOF\n", "labels": {"reads": [{"table": "spacecraft", "columns": ["response_time", "accreditation_type", "active", "rating_in_percent"]}], "writes": [{"table": "military_technology_projects", "columns": ["response_time", "accreditation_type", "active", "rating_in_percent"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO biosensors.projects SELECT element_id, title FROM marine_mammals WHERE element_id > 195\");\n", "labels": {"reads": [{"table": "marine_mammals", "columns": ["element_id", "title"]}], "writes": [{"table": "biosensors.projects", "columns": ["element_id", "title"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ads_sessions_di --columns meal_id,acidification_level --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ads_sessions_di", "columns": ["meal_id", "acidification_level"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model item_prices depends on voting_record\ndbt build -s item_prices --vars 'source: voting_record'\n", "labels": {"reads": [{"table": "voting_record", "columns": null}], "writes": [{"table": "item_prices", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 214;\nSQL\n", "labels": {"reads": [{"table": "state_water_usage", "columns": ["faculty", "other_item_details"]}, {"table": "solar_farms", "columns": ["saledate", "client_first_name", "medical_condition"]}], "writes": [{"table": "workers", "columns": ["saledate", "client_first_name", "medical_condition"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT schedule, employeeid FROM precision_farming_imagery LIMIT 17\")\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO australia_offset_programs SELECT statement_details, company_id, hardware_model_name, room_count FROM canada_cosmetics_preferences WHERE statement_details > 453\")\n", "labels": {"reads": [{"table": "precision_farming_imagery", "columns": ["schedule", "employeeid"]}, {"table": "canada_cosmetics_preferences", "columns": ["statement_details", "company_id", "hardware_model_name", "room_count"]}], "writes": [{"table": "australia_offset_programs", "columns": ["statement_details", "company_id", "hardware_model_name", "room_count"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO consumer_preference SELECT session_id, num_pallets, totalamount, warehousename FROM functional_areas WHERE session_id > 28\");\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["session_id", "num_pallets", "totalamount", "warehousename"]}], "writes": [{"table": "consumer_preference", "columns": ["session_id", "num_pallets", "totalamount", "warehousename"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"forest\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "forest", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"useracct\").toPandas()\ndf[[\"policy_area\", \"zip\"]].to_sql(\"available_policies\", engine, index=False)\n", "labels": {"reads": [{"table": "useracct", "columns": null}], "writes": [{"table": "available_policies", "columns": ["policy_area", "zip"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"public_transport.passenger_count\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "public_transport.passenger_count", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT author_id, chemical_type FROM productivity LIMIT 217\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO player_demographics SELECT theftdate, sighting_date, meter_200, duration_ms FROM solar_energy WHERE theftdate > 106\")\n", "labels": {"reads": [{"table": "productivity", "columns": ["author_id", "chemical_type"]}, {"table": "solar_energy", "columns": ["theftdate", "sighting_date", "meter_200", "duration_ms"]}], "writes": [{"table": "player_demographics", "columns": ["theftdate", "sighting_date", "meter_200", "duration_ms"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO communityengagement SELECT * FROM legacy\nspark.sql(\"INSERT INTO archaeologists SELECT gamepreference, game, post_date, enddate FROM dw.shipments_df WHERE gamepreference > 148\")\n", "labels": {"reads": [{"table": "dw.shipments_df", "columns": ["gamepreference", "game", "post_date", "enddate"]}], "writes": [{"table": "archaeologists", "columns": ["gamepreference", "game", "post_date", "enddate"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_member_point --columns shipment_date,shelter_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_member_point", "columns": ["shipment_date", "shelter_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 251;\nSQL\n", "labels": {"reads": [{"table": "product_sales", "columns": ["contract_count", "produceid"]}, {"table": "water_treatment_facilities", "columns": ["species_name", "customer", "city_traffic_speed"]}], "writes": [{"table": "project_issues", "columns": ["species_name", "customer", "city_traffic_speed"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM recycling_rates\", conn)\ndf.to_sql(\"emergency_calls\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "recycling_rates", "columns": null}], "writes": [{"table": "emergency_calls", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model sustainable_warehouses depends on bi.bi_risk_score_df\ndbt build -s sustainable_warehouses --vars '{\"src\":\"bi.bi_risk_score_df\"}'\n", "labels": {"reads": [{"table": "bi.bi_risk_score_df", "columns": null}], "writes": [{"table": "sustainable_warehouses", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO mart.inventory_hourly SELECT invoice_date, claimamount, vendorid FROM investment_rounds WHERE invoice_date > 75\"\n", "labels": {"reads": [{"table": "investment_rounds", "columns": ["invoice_date", "claimamount", "vendorid"]}], "writes": [{"table": "mart.inventory_hourly", "columns": ["invoice_date", "claimamount", "vendorid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO productivity (mission_name, excavation_site) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "productivity", "columns": ["mission_name", "excavation_site"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"gameattendance\")\nsrc.write.insertInto(\"housingaffordability\", overwrite=True)\n", "labels": {"reads": [{"table": "gameattendance", "columns": null}], "writes": [{"table": "housingaffordability", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO menu_item SELECT emp_fname, last_maintenance FROM manufacturermaterials WHERE emp_fname > 306\"\n", "labels": {"reads": [{"table": "manufacturermaterials", "columns": ["emp_fname", "last_maintenance"]}], "writes": [{"table": "menu_item", "columns": ["emp_fname", "last_maintenance"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ytterbiumproduction\");\ndf.write().mode(\"overwrite\").saveAsTable(\"store\");\n", "labels": {"reads": [{"table": "ytterbiumproduction", "columns": null}], "writes": [{"table": "store", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO program_outcomes (fine, dependent_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "program_outcomes", "columns": ["fine", "dependent_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"militarypatents\").toPandas()\ndf[[\"owner_id\", \"representative_name\"]].to_sql(\"urban_initiatives\", engine, index=False)\n", "labels": {"reads": [{"table": "militarypatents", "columns": null}], "writes": [{"table": "urban_initiatives", "columns": ["owner_id", "representative_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO department_publications SELECT a.well_id, b.room_count FROM ods.ods_users_di a JOIN workerbuildings b ON a.plant_location = b.plant_location\"\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": null}, {"table": "workerbuildings", "columns": null}], "writes": [{"table": "department_publications", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT chair_name, institution FROM port_office LIMIT 197\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "port_office", "columns": ["chair_name", "institution"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO building_permits SELECT date_of_birth, water_temp, role_name FROM recalls WHERE date_of_birth > 251\"], check=True)\n", "labels": {"reads": [{"table": "recalls", "columns": ["date_of_birth", "water_temp", "role_name"]}], "writes": [{"table": "building_permits", "columns": ["date_of_birth", "water_temp", "role_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM spacecraft_components\", conn)\ndf.to_sql(\"stg.stg_risk_score_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "spacecraft_components", "columns": null}], "writes": [{"table": "stg.stg_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"orders\")\nupsert_to_sink(df, \"insurancetype\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "orders", "columns": null}], "writes": [{"table": "insurancetype", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ods.ods_coupon_use_delta\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ods.ods_coupon_use_delta", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO brandrevenue SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 213;\nSQL\n", "labels": {"reads": [{"table": "payments", "columns": ["asset_id", "prod_date"]}, {"table": "shipment", "columns": ["engagement_count", "accreditation_level", "chemical"]}], "writes": [{"table": "platformh", "columns": ["engagement_count", "accreditation_level", "chemical"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO electric_buses SELECT order_item_id, wastetype, payment_date FROM circuits WHERE order_item_id > 136\"], check=True)\n", "labels": {"reads": [{"table": "circuits", "columns": ["order_item_id", "wastetype", "payment_date"]}], "writes": [{"table": "electric_buses", "columns": ["order_item_id", "wastetype", "payment_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"assignedto\")\n", "labels": {"reads": [{"table": "ai_projects", "columns": null}], "writes": [{"table": "assignedto", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws_clicks_di (permitid, numhearings) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws_clicks_di", "columns": ["permitid", "numhearings"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.class > 494).all()\n# src table: cases\nengine.execute(\"INSERT INTO cybersecurity_vulnerabilities SELECT * FROM cases\")\n", "labels": {"reads": [{"table": "cases", "columns": null}], "writes": [{"table": "cybersecurity_vulnerabilities", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"renewabletypes\");\ndf.write().mode(\"overwrite\").saveAsTable(\"temperature_data\");\n", "labels": {"reads": [{"table": "renewabletypes", "columns": null}], "writes": [{"table": "temperature_data", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO mart.mart_device_log SELECT flag, digital_asset, affirmative FROM game_scores WHERE flag > 86\")\n", "labels": {"reads": [{"table": "game_scores", "columns": ["flag", "digital_asset", "affirmative"]}], "writes": [{"table": "mart.mart_device_log", "columns": ["flag", "digital_asset", "affirmative"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO animal_budget SELECT a.billing, b.product_type FROM visits a JOIN industrial_building_energy_efficiency b ON a.engagementid = b.engagementid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "visits", "columns": null}, {"table": "industrial_building_energy_efficiency", "columns": null}], "writes": [{"table": "animal_budget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table sourcing --target-dir /tmp/land\n", "labels": {"reads": [{"table": "sourcing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 66;\nEOF\n", "labels": {"reads": [{"table": "overwatch_scores", "columns": ["awards", "shipmenttype"]}], "writes": [{"table": "cybersecurity_strategies", "columns": ["awards", "shipmenttype"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT guest_id, productiondate FROM ucl_top10\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"market\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ucl_top10", "columns": ["guest_id", "productiondate"]}], "writes": [{"table": "market", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.mgr_start_date > 499).all()\n# src table: crop_yield\nengine.execute(\"INSERT INTO mart.mart_payments_df SELECT * FROM crop_yield\")\n", "labels": {"reads": [{"table": "crop_yield", "columns": null}], "writes": [{"table": "mart.mart_payments_df", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO genetic.projects SELECT a.brand_mentioned, b.company_name FROM stg.risk_score_di a JOIN staff_roles b ON a.is_safe = b.is_safe\"\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": null}, {"table": "staff_roles", "columns": null}], "writes": [{"table": "genetic.projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"city_labor_cost\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws_events_di\");\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": null}], "writes": [{"table": "dws_events_di", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"usdaviolations\").toPandas()\ndf[[\"studentid\", \"numhearings\"]].to_sql(\"freshwater_fish_farms\", engine, index=False)\n", "labels": {"reads": [{"table": "usdaviolations", "columns": null}], "writes": [{"table": "freshwater_fish_farms", "columns": ["studentid", "numhearings"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO industrial_building_energy_efficiency SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO vr_adopters SELECT produceid, stu_dob FROM public_schools WHERE produceid > 57\")\n", "labels": {"reads": [{"table": "public_schools", "columns": ["produceid", "stu_dob"]}], "writes": [{"table": "vr_adopters", "columns": ["produceid", "stu_dob"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO biomes (deliverydate, waste_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "biomes", "columns": ["deliverydate", "waste_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT max_dissolved_oxygen, meter_200 FROM constructorstandings LIMIT 134\")\nimport logging\nspark.sql(\"INSERT INTO user_profiles SELECT loan_amount, researcher_id FROM dw.inventory_delta WHERE loan_amount > 233\")\n", "labels": {"reads": [{"table": "constructorstandings", "columns": ["max_dissolved_oxygen", "meter_200"]}, {"table": "dw.inventory_delta", "columns": ["loan_amount", "researcher_id"]}], "writes": [{"table": "user_profiles", "columns": ["loan_amount", "researcher_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO inclusive_housing (log_entry_description, employee_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "inclusive_housing", "columns": ["log_entry_description", "employee_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO co2_emissions SELECT share_count, num_projects FROM platformh WHERE share_count > 434\");\n", "labels": {"reads": [{"table": "platformh", "columns": ["share_count", "num_projects"]}], "writes": [{"table": "co2_emissions", "columns": ["share_count", "num_projects"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"efforts\")\nsrc.write.insertInto(\"virtual_tour_stats\", overwrite=True)\n", "labels": {"reads": [{"table": "efforts", "columns": null}], "writes": [{"table": "virtual_tour_stats", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"communityengagementmetrics\").toPandas()\ndf[[\"serviceid\", \"chair_name\"]].to_sql(\"mart.mart_users_di\", engine, index=False)\n", "labels": {"reads": [{"table": "communityengagementmetrics", "columns": null}], "writes": [{"table": "mart.mart_users_di", "columns": ["serviceid", "chair_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"startup_founders\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "startup_founders", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO equipment SELECT * FROM legacy\nspark.sql(\"INSERT INTO vessels_2 SELECT leadershiptraining, case_id, attorney_last_name, sales_count FROM creative_ai WHERE leadershiptraining > 93\")\n", "labels": {"reads": [{"table": "creative_ai", "columns": ["leadershiptraining", "case_id", "attorney_last_name", "sales_count"]}], "writes": [{"table": "vessels_2", "columns": ["leadershiptraining", "case_id", "attorney_last_name", "sales_count"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 230;\nSQL\n", "labels": {"reads": [{"table": "movie_financials", "columns": ["heart_rate", "vaccine_type"]}, {"table": "publicchargingstations", "columns": ["emp_dob", "green_building_id"]}], "writes": [{"table": "complaints", "columns": ["emp_dob", "green_building_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT restypedescription, node_id FROM iron LIMIT 363\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO musicgenre SELECT investmenttype, tv_show_id FROM caseattorneys WHERE investmenttype > 234\")\n", "labels": {"reads": [{"table": "iron", "columns": ["restypedescription", "node_id"]}, {"table": "caseattorneys", "columns": ["investmenttype", "tv_show_id"]}], "writes": [{"table": "musicgenre", "columns": ["investmenttype", "tv_show_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model rent_arrears depends on bioreactor\ndbt run --models rent_arrears --vars '{\"source_table\":\"bioreactor\"}'\n", "labels": {"reads": [{"table": "bioreactor", "columns": null}], "writes": [{"table": "rent_arrears", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO continents SELECT streams, primaryaffiliation, staff_address_id FROM mental_health_professionals_2 WHERE streams > 299\"\n", "labels": {"reads": [{"table": "mental_health_professionals_2", "columns": ["streams", "primaryaffiliation", "staff_address_id"]}], "writes": [{"table": "continents", "columns": ["streams", "primaryaffiliation", "staff_address_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table agricultural_innovation_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "agricultural_innovation_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ca_menu_items --columns itemname,labor_practice --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ca_menu_items", "columns": ["itemname", "labor_practice"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mining.company SELECT part_id, service_details, initiative_type FROM deep_sea_species WHERE part_id > 216\"\n", "labels": {"reads": [{"table": "deep_sea_species", "columns": ["part_id", "service_details", "initiative_type"]}], "writes": [{"table": "mining.company", "columns": ["part_id", "service_details", "initiative_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT claim_id, credit_score FROM campaigns\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"jupiter_missions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "campaigns", "columns": ["claim_id", "credit_score"]}], "writes": [{"table": "jupiter_missions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT exhibit_location, circuitid FROM tvshows\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"genre_songs\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "tvshows", "columns": ["exhibit_location", "circuitid"]}], "writes": [{"table": "genre_songs", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO danceevents SELECT a.start_therapy, b.dockingdate FROM waterconservation a JOIN head b ON a.subject_id = b.subject_id\"\n", "labels": {"reads": [{"table": "waterconservation", "columns": null}, {"table": "head", "columns": null}], "writes": [{"table": "danceevents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"document_types\").toPandas()\ndf[[\"menu_type\", \"ll_activity\"]].to_sql(\"open_pedagogy_courses\", engine, index=False)\n", "labels": {"reads": [{"table": "document_types", "columns": null}], "writes": [{"table": "open_pedagogy_courses", "columns": ["menu_type", "ll_activity"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO genetics_stats.research_projects SELECT founding_location, risk_score, complaint_id, license_type FROM e_scooter_trips WHERE founding_location > 443\"\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": ["founding_location", "risk_score", "complaint_id", "license_type"]}], "writes": [{"table": "genetics_stats.research_projects", "columns": ["founding_location", "risk_score", "complaint_id", "license_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO genre_songs SELECT content_type, routeid FROM artcollection WHERE content_type > 258\")\n", "labels": {"reads": [{"table": "artcollection", "columns": ["content_type", "routeid"]}], "writes": [{"table": "genre_songs", "columns": ["content_type", "routeid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM environmental_impact_stats\"\n", "labels": {"reads": [{"table": "environmental_impact_stats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mining_companies\", conn)\ndf.to_sql(\"player_f\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mining_companies", "columns": null}], "writes": [{"table": "player_f", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marathons\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_orders_full\")\n", "labels": {"reads": [{"table": "marathons", "columns": null}], "writes": [{"table": "ads.ads_orders_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.coupon_use --columns has_parabens,projecttype --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.coupon_use", "columns": ["has_parabens", "projecttype"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT make, birth_place FROM patents LIMIT 278\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "patents", "columns": ["make", "birth_place"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM skills\"\n", "labels": {"reads": [{"table": "skills", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO furniture (exhibitions, donator_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "furniture", "columns": ["exhibitions", "donator_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mining_operation_data SELECT device_id, parent_organization_id, retailer FROM ods.sessions_daily WHERE device_id > 13\"], check=True)\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": ["device_id", "parent_organization_id", "retailer"]}], "writes": [{"table": "mining_operation_data", "columns": ["device_id", "parent_organization_id", "retailer"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO units SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"renewables.renewable_projects\")\nwrite_to_sink(df, \"electric_buses\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "renewables.renewable_projects", "columns": null}], "writes": [{"table": "electric_buses", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT school, appointment_duration FROM innovation_metrics LIMIT 251\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "innovation_metrics", "columns": ["school", "appointment_duration"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sales_region SELECT * FROM legacy\ncur.execute(\"SELECT fare_amount, issue FROM wholesale_orders LIMIT 368\")\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": ["fare_amount", "issue"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT client_id, reviewscore FROM investor\", engine)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"school_enrollment\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "investor", "columns": ["client_id", "reviewscore"]}], "writes": [{"table": "school_enrollment", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 395;\nSQL\n", "labels": {"reads": [{"table": "artwork", "columns": ["category_id", "savingsid"]}, {"table": "mineral_extraction", "columns": ["launch_company", "ironid"]}], "writes": [{"table": "playersessions", "columns": ["launch_company", "ironid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tech_workers_union\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model wind_farms depends on disease_prevalence\ndbt build -s wind_farms --vars 'source: disease_prevalence'\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": null}], "writes": [{"table": "wind_farms", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM document_sections_images\", conn)\ndf.to_sql(\"smartcityprojects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "document_sections_images", "columns": null}], "writes": [{"table": "smartcityprojects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO inspectiondata SELECT feedtype, nominee FROM esa_missions WHERE feedtype > 72\")\n", "labels": {"reads": [{"table": "esa_missions", "columns": ["feedtype", "nominee"]}], "writes": [{"table": "inspectiondata", "columns": ["feedtype", "nominee"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO restorative_justice_sentences SELECT a.era, b.denomination FROM seafood a JOIN musicsales b ON a.risk_level = b.risk_level\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "seafood", "columns": null}, {"table": "musicsales", "columns": null}], "writes": [{"table": "restorative_justice_sentences", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"exhibition\")\nsrc.write.insertInto(\"item\", overwrite=True)\n", "labels": {"reads": [{"table": "exhibition", "columns": null}], "writes": [{"table": "item", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tourist_destinations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tourist_destinations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"gamesessions\").where(\"dt = current_date()\").writeTo(\"dysprosiumproduction\").append()\n", "labels": {"reads": [{"table": "gamesessions", "columns": null}], "writes": [{"table": "dysprosiumproduction", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO project SELECT production_volume, sales_count, staff_last_name FROM region_stats WHERE production_volume > 367\");\n", "labels": {"reads": [{"table": "region_stats", "columns": ["production_volume", "sales_count", "staff_last_name"]}], "writes": [{"table": "project", "columns": ["production_volume", "sales_count", "staff_last_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO lenders SELECT floor_area_m2, content_type, court_appearances FROM diversification_projects WHERE floor_area_m2 > 78\")\n", "labels": {"reads": [{"table": "diversification_projects", "columns": ["floor_area_m2", "content_type", "court_appearances"]}], "writes": [{"table": "lenders", "columns": ["floor_area_m2", "content_type", "court_appearances"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO classicgame SELECT enable_location_tracking, university, street_address, membership_id FROM genetics.crispr WHERE enable_location_tracking > 459\")\n", "labels": {"reads": [{"table": "genetics.crispr", "columns": ["enable_location_tracking", "university", "street_address", "membership_id"]}], "writes": [{"table": "classicgame", "columns": ["enable_location_tracking", "university", "street_address", "membership_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM co2_emissions\"\n", "labels": {"reads": [{"table": "co2_emissions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO workouts SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO open_data_initiatives SELECT tour_type, next_entry_id FROM foodaid WHERE tour_type > 49\");\n", "labels": {"reads": [{"table": "foodaid", "columns": ["tour_type", "next_entry_id"]}], "writes": [{"table": "open_data_initiatives", "columns": ["tour_type", "next_entry_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 214;\nSQL\n", "labels": {"reads": [{"table": "disasters", "columns": ["sculpture_name", "daily_distance"]}, {"table": "membership_register_branch", "columns": ["facid", "diversity_score"]}], "writes": [{"table": "community_health_centers", "columns": ["facid", "diversity_score"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"convictions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "convictions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model neodymium_prices depends on concentrateprices\ndbt build --models neodymium_prices --vars '{\"source_table\":\"concentrateprices\"}'\n", "labels": {"reads": [{"table": "concentrateprices", "columns": null}], "writes": [{"table": "neodymium_prices", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO domesticconferences SELECT regionname, cityid, sale_year FROM innovation_grants WHERE regionname > 133\"\n", "labels": {"reads": [{"table": "innovation_grants", "columns": ["regionname", "cityid", "sale_year"]}], "writes": [{"table": "domesticconferences", "columns": ["regionname", "cityid", "sale_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 54;\nSQL\n", "labels": {"reads": [{"table": "document_types", "columns": ["date_claim_made", "framework_id"]}, {"table": "all_star", "columns": ["apt_id", "spacecraft", "subscriber_id"]}], "writes": [{"table": "project_timeline", "columns": ["apt_id", "spacecraft", "subscriber_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_sessions_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"medical_facilities\")\n", "labels": {"reads": [{"table": "bi.bi_sessions_daily", "columns": null}], "writes": [{"table": "medical_facilities", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM submission\", conn)\ndf.to_sql(\"vehicle_maintenance\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "submission", "columns": null}], "writes": [{"table": "vehicle_maintenance", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO microfinance_clients SELECT labor_hour_id, weeks_on_top FROM busmaintenance WHERE labor_hour_id > 496\")\n", "labels": {"reads": [{"table": "busmaintenance", "columns": ["labor_hour_id", "weeks_on_top"]}], "writes": [{"table": "microfinance_clients", "columns": ["labor_hour_id", "weeks_on_top"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO channel SELECT has_parabens, avg_yield, fair_labor FROM electricvehicleadoption WHERE has_parabens > 467\")\n", "labels": {"reads": [{"table": "electricvehicleadoption", "columns": ["has_parabens", "avg_yield", "fair_labor"]}], "writes": [{"table": "channel", "columns": ["has_parabens", "avg_yield", "fair_labor"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"volume\");\ndf.write().mode(\"overwrite\").saveAsTable(\"safety_incidents\");\n", "labels": {"reads": [{"table": "volume", "columns": null}], "writes": [{"table": "safety_incidents", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"justice_schemas.legal_tech_providers\").toPandas()\ndf[[\"no_of_customers\", \"trend_id\"]].to_sql(\"geothermal_power_plants\", engine, index=False)\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": null}], "writes": [{"table": "geothermal_power_plants", "columns": ["no_of_customers", "trend_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw.shipments_df\")\nsrc.write.insertInto(\"dws.dws_refunds_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "dw.shipments_df", "columns": null}], "writes": [{"table": "dws.dws_refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO exhibitiondetails (impactid, totalprice) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "exhibitiondetails", "columns": ["impactid", "totalprice"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO agricultural_innovation_projects SELECT text_of_notes, amount_due, centername FROM precision_farming_imagery WHERE text_of_notes > 55\");\n", "labels": {"reads": [{"table": "precision_farming_imagery", "columns": ["text_of_notes", "amount_due", "centername"]}], "writes": [{"table": "agricultural_innovation_projects", "columns": ["text_of_notes", "amount_due", "centername"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO attack_outcomes SELECT staff_details, chemical_type FROM community_leaders WHERE staff_details > 47\")\n", "labels": {"reads": [{"table": "community_leaders", "columns": ["staff_details", "chemical_type"]}], "writes": [{"table": "attack_outcomes", "columns": ["staff_details", "chemical_type"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO elements_price SELECT matchdate, customer_id, org_name FROM genetics.projects WHERE matchdate > 222\");\n", "labels": {"reads": [{"table": "genetics.projects", "columns": ["matchdate", "customer_id", "org_name"]}], "writes": [{"table": "elements_price", "columns": ["matchdate", "customer_id", "org_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT don_id, transaction_volume FROM ods_products_delta\", engine)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nimport logging\ndf.to_sql(\"labor_cost\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": ["don_id", "transaction_volume"]}], "writes": [{"table": "labor_cost", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM player\", conn)\ndf.to_sql(\"tournaments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "player", "columns": null}], "writes": [{"table": "tournaments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"wildlife_habitats\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "wildlife_habitats", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM impact_investments\", conn)\ndf.to_sql(\"patient\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "impact_investments", "columns": null}], "writes": [{"table": "patient", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM exit_strategy\", conn)\ndf.to_sql(\"stock_levels\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "exit_strategy", "columns": null}], "writes": [{"table": "stock_levels", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sustainable_practices\", conn)\ndf.to_sql(\"songs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sustainable_practices", "columns": null}], "writes": [{"table": "songs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT incident_description, organization_name FROM az_drought_impact\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"threats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "az_drought_impact", "columns": ["incident_description", "organization_name"]}], "writes": [{"table": "threats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT acidification_level, workshop_group_id FROM bay_area_properties LIMIT 259\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "bay_area_properties", "columns": ["acidification_level", "workshop_group_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.position > 207).all()\n# src table: union_membership\nengine.execute(\"INSERT INTO exit_strategy SELECT * FROM union_membership\")\n", "labels": {"reads": [{"table": "union_membership", "columns": null}], "writes": [{"table": "exit_strategy", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO dw.shipments_di SELECT generation_date, uid, label_id FROM submarine_canyons WHERE generation_date > 500\"\n", "labels": {"reads": [{"table": "submarine_canyons", "columns": ["generation_date", "uid", "label_id"]}], "writes": [{"table": "dw.shipments_di", "columns": ["generation_date", "uid", "label_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM intelligenceoperations\"\n", "labels": {"reads": [{"table": "intelligenceoperations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"projects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "projects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"postseason\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "postseason", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tourism_activities\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tourism_activities", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO co2emissions (movement, party_email) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "co2emissions", "columns": ["movement", "party_email"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 428;\nEOF\n", "labels": {"reads": [{"table": "model_data", "columns": ["inspection_date", "booking_status_code", "lname"]}], "writes": [{"table": "pollution_initiatives", "columns": ["inspection_date", "booking_status_code", "lname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO producers (bedroom_count, route_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "producers", "columns": ["bedroom_count", "route_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads_sessions_di SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"urban_transportation\");\ndf.write().mode(\"overwrite\").saveAsTable(\"zipcodes\");\n", "labels": {"reads": [{"table": "urban_transportation", "columns": null}], "writes": [{"table": "zipcodes", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model exposure_df depends on russia_nato_diplomacy\ndbt run --models exposure_df --vars 'source: russia_nato_diplomacy'\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": null}], "writes": [{"table": "exposure_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"collectivebargaining\");\ndf.write().mode(\"overwrite\").saveAsTable(\"safetytests\");\n", "labels": {"reads": [{"table": "collectivebargaining", "columns": null}], "writes": [{"table": "safetytests", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO beverages SELECT subscription_start_date, account_balance FROM usdaviolations WHERE subscription_start_date > 247\")\n", "labels": {"reads": [{"table": "usdaviolations", "columns": ["subscription_start_date", "account_balance"]}], "writes": [{"table": "beverages", "columns": ["subscription_start_date", "account_balance"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO regions SELECT ai_algorithm_id, industry FROM dw_payments WHERE ai_algorithm_id > 205\"\n", "labels": {"reads": [{"table": "dw_payments", "columns": ["ai_algorithm_id", "industry"]}], "writes": [{"table": "regions", "columns": ["ai_algorithm_id", "industry"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dw.dw_risk_score_full SELECT start, building_full_name FROM tech_workers_union WHERE start > 162\"\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": ["start", "building_full_name"]}], "writes": [{"table": "dw.dw_risk_score_full", "columns": ["start", "building_full_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"labor_practices\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dwd_coupon_use_hourly\")\n", "labels": {"reads": [{"table": "labor_practices", "columns": null}], "writes": [{"table": "dwd_coupon_use_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT mar, grant_id FROM stores LIMIT 74\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO peacekeeping_units SELECT death_year, schedule_id, member_name, noise_level FROM vocals WHERE death_year > 440\")\n", "labels": {"reads": [{"table": "stores", "columns": ["mar", "grant_id"]}, {"table": "vocals", "columns": ["death_year", "schedule_id", "member_name", "noise_level"]}], "writes": [{"table": "peacekeeping_units", "columns": ["death_year", "schedule_id", "member_name", "noise_level"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"support\").toPandas()\ndf[[\"founding_date\", \"donationid\"]].to_sql(\"party_services\", engine, index=False)\n", "labels": {"reads": [{"table": "support", "columns": null}], "writes": [{"table": "party_services", "columns": ["founding_date", "donationid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"miningwaterusage\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "miningwaterusage", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO threat_intelligence SELECT energy_efficiency_kwh_m2_year, access_count, credit_score, trench_id FROM contract_negotiations WHERE energy_efficiency_kwh_m2_year > 44\"\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": ["energy_efficiency_kwh_m2_year", "access_count", "credit_score", "trench_id"]}], "writes": [{"table": "threat_intelligence", "columns": ["energy_efficiency_kwh_m2_year", "access_count", "credit_score", "trench_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"makeup_products\");\ndf.write().mode(\"overwrite\").saveAsTable(\"beverages\");\n", "labels": {"reads": [{"table": "makeup_products", "columns": null}], "writes": [{"table": "beverages", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bi.bi_exposure_hourly\").where(\"dt = current_date()\").writeTo(\"companies\").append()\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": null}], "writes": [{"table": "companies", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO legalaidrequests SELECT quantity_sold, loadingend, starting_year, museum FROM dws.dws_events_hourly WHERE quantity_sold > 202\"\n", "labels": {"reads": [{"table": "dws.dws_events_hourly", "columns": ["quantity_sold", "loadingend", "starting_year", "museum"]}], "writes": [{"table": "legalaidrequests", "columns": ["quantity_sold", "loadingend", "starting_year", "museum"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO ocean_depths SELECT exhibition_id, initiative, committee FROM safetyorgs WHERE exhibition_id > 276\");\n", "labels": {"reads": [{"table": "safetyorgs", "columns": ["exhibition_id", "initiative", "committee"]}], "writes": [{"table": "ocean_depths", "columns": ["exhibition_id", "initiative", "committee"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT bus_number, creator FROM pets\", engine)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"hotel_chains\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "pets", "columns": ["bus_number", "creator"]}], "writes": [{"table": "hotel_chains", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stars\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"shipment_data\")\n", "labels": {"reads": [{"table": "stars", "columns": null}], "writes": [{"table": "shipment_data", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dwd.dwd_inventory_hourly SELECT store_id, base_name FROM workerbuildings WHERE store_id > 194\"\n", "labels": {"reads": [{"table": "workerbuildings", "columns": ["store_id", "base_name"]}], "writes": [{"table": "dwd.dwd_inventory_hourly", "columns": ["store_id", "base_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.vendor_state > 21).all()\n# src table: factories\nengine.execute(\"INSERT INTO tourism_activities SELECT * FROM factories\")\n", "labels": {"reads": [{"table": "factories", "columns": null}], "writes": [{"table": "tourism_activities", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fruitimport SELECT sustainability_initiative_id, order_status, founding_location FROM wildlife WHERE sustainability_initiative_id > 10\"\n", "labels": {"reads": [{"table": "wildlife", "columns": ["sustainability_initiative_id", "order_status", "founding_location"]}], "writes": [{"table": "fruitimport", "columns": ["sustainability_initiative_id", "order_status", "founding_location"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.dwd_campaigns (total_investment, salesperson) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_campaigns", "columns": ["total_investment", "salesperson"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO evsales (shale_play, annual_carbon_offsets) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "evsales", "columns": ["shale_play", "annual_carbon_offsets"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO marine_life_research_stations SELECT visit_date, mean_visibility_miles, menu_type FROM carbon_sequestration WHERE visit_date > 209\"\n", "labels": {"reads": [{"table": "carbon_sequestration", "columns": ["visit_date", "mean_visibility_miles", "menu_type"]}], "writes": [{"table": "marine_life_research_stations", "columns": ["visit_date", "mean_visibility_miles", "menu_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table indigenous_communities --columns release_year,granteeid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "indigenous_communities", "columns": ["release_year", "granteeid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"stg.stg_users_daily\")\nwrite_to_sink(df, \"complaints_breakdown\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.stg_users_daily", "columns": null}], "writes": [{"table": "complaints_breakdown", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table smartcitycosts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO bridgeconstruction SELECT * FROM legacy\nspark.sql(\"INSERT INTO channel SELECT market_share, priceid, organization_details, job_title FROM bi_shipments_daily WHERE market_share > 145\")\n", "labels": {"reads": [{"table": "bi_shipments_daily", "columns": ["market_share", "priceid", "organization_details", "job_title"]}], "writes": [{"table": "channel", "columns": ["market_share", "priceid", "organization_details", "job_title"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO producersnewmexico SELECT * FROM legacy\nspark.sql(\"INSERT INTO stg.stg_risk_score_hourly SELECT media_literacy_score, port_id, price_in_dollars FROM climate_communication_projects WHERE media_literacy_score > 389\")\n", "labels": {"reads": [{"table": "climate_communication_projects", "columns": ["media_literacy_score", "port_id", "price_in_dollars"]}], "writes": [{"table": "stg.stg_risk_score_hourly", "columns": ["media_literacy_score", "port_id", "price_in_dollars"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ref_document_status SELECT delegate, is_public FROM new_schedules WHERE delegate > 220\"\n", "labels": {"reads": [{"table": "new_schedules", "columns": ["delegate", "is_public"]}], "writes": [{"table": "ref_document_status", "columns": ["delegate", "is_public"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO problem_log (running_time, posts_per_day) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "problem_log", "columns": ["running_time", "posts_per_day"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO school_roster SELECT attorney_id, race_ethnicity FROM staff_department_assignments WHERE attorney_id > 164\"], check=True)\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["attorney_id", "race_ethnicity"]}], "writes": [{"table": "school_roster", "columns": ["attorney_id", "race_ethnicity"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM subscribers\"\n", "labels": {"reads": [{"table": "subscribers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stg.inventory_df\");\ndf.write().mode(\"overwrite\").saveAsTable(\"artprograms\");\n", "labels": {"reads": [{"table": "stg.inventory_df", "columns": null}], "writes": [{"table": "artprograms", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO revenue SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.bi_payments_delta SELECT frameworkcountry, mine_name, iata, laborproductivity FROM ads.events WHERE frameworkcountry > 64\"\n", "labels": {"reads": [{"table": "ads.events", "columns": ["frameworkcountry", "mine_name", "iata", "laborproductivity"]}], "writes": [{"table": "bi.bi_payments_delta", "columns": ["frameworkcountry", "mine_name", "iata", "laborproductivity"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 67;\nEOF\n", "labels": {"reads": [{"table": "fraud_detections", "columns": ["mission_date", "typical_buying_price", "exhibitionname"]}], "writes": [{"table": "healthcare_centers", "columns": ["mission_date", "typical_buying_price", "exhibitionname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"results\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"contract_states\")\n", "labels": {"reads": [{"table": "results", "columns": null}], "writes": [{"table": "contract_states", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"talent_acquisition\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"unionnegotiations\")\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": null}], "writes": [{"table": "unionnegotiations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"divisions\").where(\"dt = current_date()\").writeTo(\"home_game\").append()\n", "labels": {"reads": [{"table": "divisions", "columns": null}], "writes": [{"table": "home_game", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd_coupon_use_hourly\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dwd_coupon_use_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO departments SELECT * FROM legacy\nspark.sql(\"INSERT INTO family_cases SELECT total_distance, disease, cultivatorname FROM ai_safety_incidents WHERE total_distance > 342\")\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": ["total_distance", "disease", "cultivatorname"]}], "writes": [{"table": "family_cases", "columns": ["total_distance", "disease", "cultivatorname"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"meals\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"artistsdemographics\")\n", "labels": {"reads": [{"table": "meals", "columns": null}], "writes": [{"table": "artistsdemographics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM country_waste_generation\", conn)\ndf.to_sql(\"genre\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "country_waste_generation", "columns": null}], "writes": [{"table": "genre", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO hotel_tech_adoptions SELECT year_join, next_entry_id FROM dws_coupon_use WHERE year_join > 71\")\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["year_join", "next_entry_id"]}], "writes": [{"table": "hotel_tech_adoptions", "columns": ["year_join", "next_entry_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT trench_id, minutes FROM carbon_offset_programs\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"budget\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": ["trench_id", "minutes"]}], "writes": [{"table": "budget", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO australia_offset_programs SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_fabrics\").toPandas()\ndf[[\"value_points\", \"usage\"]].to_sql(\"vrgames\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": null}], "writes": [{"table": "vrgames", "columns": ["value_points", "usage"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"tracklists\").where(\"dt = current_date()\").writeTo(\"user_reactions\").append()\n", "labels": {"reads": [{"table": "tracklists", "columns": null}], "writes": [{"table": "user_reactions", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO dwd.dwd_orders_daily SELECT individual_last_name, field_name, emp_fname, membername FROM aquatic_farms WHERE individual_last_name > 292\")\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": ["individual_last_name", "field_name", "emp_fname", "membername"]}], "writes": [{"table": "dwd.dwd_orders_daily", "columns": ["individual_last_name", "field_name", "emp_fname", "membername"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi_device_log_daily SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO hydro_plants SELECT a.home_team_score, b.purchase_date FROM volunteerprograms a JOIN bi.clicks_df b ON a.permit_date = b.permit_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "volunteerprograms", "columns": null}, {"table": "bi.clicks_df", "columns": null}], "writes": [{"table": "hydro_plants", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM recycling_rates_state\", conn)\ndf.to_sql(\"producersnewmexico\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "recycling_rates_state", "columns": null}], "writes": [{"table": "producersnewmexico", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO space_telescopes (is_vegetarian, is_unionized) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "space_telescopes", "columns": ["is_vegetarian", "is_unionized"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO evsales SELECT * FROM legacy\nspark.sql(\"INSERT INTO ods.sessions_daily SELECT start, alert_id FROM architect WHERE start > 264\")\n", "labels": {"reads": [{"table": "architect", "columns": ["start", "alert_id"]}], "writes": [{"table": "ods.sessions_daily", "columns": ["start", "alert_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO financial_transactions SELECT recorded_by_staff_id, staff_gender, deliverydate, claim_status_name FROM contract_states WHERE recorded_by_staff_id > 167\"\n", "labels": {"reads": [{"table": "contract_states", "columns": ["recorded_by_staff_id", "staff_gender", "deliverydate", "claim_status_name"]}], "writes": [{"table": "financial_transactions", "columns": ["recorded_by_staff_id", "staff_gender", "deliverydate", "claim_status_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"haircare_cruelty\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"party_events\")\n", "labels": {"reads": [{"table": "haircare_cruelty", "columns": null}], "writes": [{"table": "party_events", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO train_maintenance SELECT * FROM legacy\ncur.execute(\"SELECT cause_id, wifi FROM platform LIMIT 321\")\n", "labels": {"reads": [{"table": "platform", "columns": ["cause_id", "wifi"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO unesco_intangible_heritage SELECT a.temporary_acting, b.analysis_date FROM dw.events_hourly a JOIN climate_investments b ON a.team_id_loser = b.team_id_loser\"\n", "labels": {"reads": [{"table": "dw.events_hourly", "columns": null}, {"table": "climate_investments", "columns": null}], "writes": [{"table": "unesco_intangible_heritage", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM underwater_cables\", conn)\ndf.to_sql(\"competition\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "underwater_cables", "columns": null}], "writes": [{"table": "competition", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO measurement SELECT health_equity_metric_2, material_name FROM russia_nato_diplomacy WHERE health_equity_metric_2 > 466\")\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": ["health_equity_metric_2", "material_name"]}], "writes": [{"table": "measurement", "columns": ["health_equity_metric_2", "material_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"world_heritage_sites\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"flu_shots\")\n", "labels": {"reads": [{"table": "world_heritage_sites", "columns": null}], "writes": [{"table": "flu_shots", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table dw.dw_payments_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dw.dw_payments_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO farmers SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bikerental\").where(\"dt = current_date()\").writeTo(\"conservation_projects\").append()\n", "labels": {"reads": [{"table": "bikerental", "columns": null}], "writes": [{"table": "conservation_projects", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"low_value_contracts\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "low_value_contracts", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table volunteerhours --target-dir /tmp/land\n", "labels": {"reads": [{"table": "volunteerhours", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO regulatory_compliance (host, satelliteid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "regulatory_compliance", "columns": ["host", "satelliteid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO food_justice_orgs SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO state_usage SELECT tourist_id, health_equity_metric_3, typical_buying_price FROM mart_refunds WHERE tourist_id > 44\"\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["tourist_id", "health_equity_metric_3", "typical_buying_price"]}], "writes": [{"table": "state_usage", "columns": ["tourist_id", "health_equity_metric_3", "typical_buying_price"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO crop_temperature SELECT * FROM legacy\ncur.execute(\"SELECT faculty, health_equity_metric_2 FROM recycling_rates_state LIMIT 330\")\n", "labels": {"reads": [{"table": "recycling_rates_state", "columns": ["faculty", "health_equity_metric_2"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_life_sightings\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stg.cart_item_full\")\n", "labels": {"reads": [{"table": "marine_life_sightings", "columns": null}], "writes": [{"table": "stg.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"ods.coupon_use\")\nsrc.write.insertInto(\"impact_asia\", overwrite=True)\n", "labels": {"reads": [{"table": "ods.coupon_use", "columns": null}], "writes": [{"table": "impact_asia", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"product_details\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"city_waste_generation\")\n", "labels": {"reads": [{"table": "product_details", "columns": null}], "writes": [{"table": "city_waste_generation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fish_feed_factories SELECT a.bridgeid, b.metric FROM drought_impact a JOIN wedding b ON a.money_requested = b.money_requested\"\n", "labels": {"reads": [{"table": "drought_impact", "columns": null}, {"table": "wedding", "columns": null}], "writes": [{"table": "fish_feed_factories", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO temperature_data SELECT roomname, transaction_amount, attendee_id FROM fleet WHERE roomname > 308\"\n", "labels": {"reads": [{"table": "fleet", "columns": ["roomname", "transaction_amount", "attendee_id"]}], "writes": [{"table": "temperature_data", "columns": ["roomname", "transaction_amount", "attendee_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO obesity SELECT dphone, strain_id, request_id, target_name FROM clothing_brands WHERE dphone > 76\"\n", "labels": {"reads": [{"table": "clothing_brands", "columns": ["dphone", "strain_id", "request_id", "target_name"]}], "writes": [{"table": "obesity", "columns": ["dphone", "strain_id", "request_id", "target_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO hall_of_fame SELECT * FROM legacy\nspark.sql(\"INSERT INTO parts SELECT spf_level, community_name, operationname FROM marketingbudget WHERE spf_level > 69\")\n", "labels": {"reads": [{"table": "marketingbudget", "columns": ["spf_level", "community_name", "operationname"]}], "writes": [{"table": "parts", "columns": ["spf_level", "community_name", "operationname"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO artwork_styles SELECT a.waste_amount, b.accelerator_id FROM mobile_usage a JOIN property_community b ON a.is_organic = b.is_organic\"\n", "labels": {"reads": [{"table": "mobile_usage", "columns": null}, {"table": "property_community", "columns": null}], "writes": [{"table": "artwork_styles", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO philadelphia_police_emergencies SELECT birth_place, production_rate, used_kb, date_formed FROM country_sustainable_chains WHERE birth_place > 474\"], check=True)\n", "labels": {"reads": [{"table": "country_sustainable_chains", "columns": ["birth_place", "production_rate", "used_kb", "date_formed"]}], "writes": [{"table": "philadelphia_police_emergencies", "columns": ["birth_place", "production_rate", "used_kb", "date_formed"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM galleries\"\n", "labels": {"reads": [{"table": "galleries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT name, damage_millions_usd FROM ecohousing LIMIT 237\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ecohousing", "columns": ["name", "damage_millions_usd"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table union_membership --target-dir /tmp/land\n", "labels": {"reads": [{"table": "union_membership", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO visits_restaurant SELECT transact_date, purchaseid, totalprice FROM rural_clinics WHERE transact_date > 137\"\n", "labels": {"reads": [{"table": "rural_clinics", "columns": ["transact_date", "purchaseid", "totalprice"]}], "writes": [{"table": "visits_restaurant", "columns": ["transact_date", "purchaseid", "totalprice"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM landfill_capacity_north_america\", conn)\ndf.to_sql(\"sites_me\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "landfill_capacity_north_america", "columns": null}], "writes": [{"table": "sites_me", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.clicks_delta\").toPandas()\ndf[[\"catalog_entry_name\", \"build_year\"]].to_sql(\"cultural_events\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.clicks_delta", "columns": null}], "writes": [{"table": "cultural_events", "columns": ["catalog_entry_name", "build_year"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO clinic_2022 (culturalcompetency, dnumber) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "clinic_2022", "columns": ["culturalcompetency", "dnumber"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT guest_first_name, inspectionid FROM incarcerated LIMIT 440\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "incarcerated", "columns": ["guest_first_name", "inspectionid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT share_count, vote_percent FROM inventory\", engine)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ndf.to_sql(\"heritage_sites_3\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "inventory", "columns": ["share_count", "vote_percent"]}], "writes": [{"table": "heritage_sites_3", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO parity_violations SELECT * FROM legacy\nspark.sql(\"INSERT INTO threat_intel SELECT warehouse_state, center_name, num_solo_exhibitions, injury_date FROM sponsor_trials WHERE warehouse_state > 337\")\n", "labels": {"reads": [{"table": "sponsor_trials", "columns": ["warehouse_state", "center_name", "num_solo_exhibitions", "injury_date"]}], "writes": [{"table": "threat_intel", "columns": ["warehouse_state", "center_name", "num_solo_exhibitions", "injury_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table music_database --target-dir /tmp/land\n", "labels": {"reads": [{"table": "music_database", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 98;\nSQL\n", "labels": {"reads": [{"table": "visual_arts", "columns": ["aircraft_id", "sales_in_billion"]}, {"table": "genetics.crispr", "columns": ["department", "silver"]}], "writes": [{"table": "arrivals", "columns": ["department", "silver"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT task, hotel_id FROM wind_energy_projects\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"mars_spacecraft\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": ["task", "hotel_id"]}], "writes": [{"table": "mars_spacecraft", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO arctic_weather SELECT * FROM legacy\nspark.sql(\"INSERT INTO rural.bus_trips SELECT driller, environmental_impact_score, exhibit_location, port FROM discount_coupons WHERE driller > 11\")\n", "labels": {"reads": [{"table": "discount_coupons", "columns": ["driller", "environmental_impact_score", "exhibit_location", "port"]}], "writes": [{"table": "rural.bus_trips", "columns": ["driller", "environmental_impact_score", "exhibit_location", "port"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"restaurants_tx\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "restaurants_tx", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"state_contracts\").where(\"dt = current_date()\").writeTo(\"state_water_usage\").append()\n", "labels": {"reads": [{"table": "state_contracts", "columns": null}], "writes": [{"table": "state_water_usage", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO sustainable_tourism_practices SELECT activity_date, ship_date FROM maintenance_requests WHERE activity_date > 326\")\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": ["activity_date", "ship_date"]}], "writes": [{"table": "sustainable_tourism_practices", "columns": ["activity_date", "ship_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT target_u_id, manufacturerid FROM conservation LIMIT 476\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO sponsor_trials SELECT mappingid, co2_reduction FROM operations WHERE mappingid > 49\")\n", "labels": {"reads": [{"table": "conservation", "columns": ["target_u_id", "manufacturerid"]}, {"table": "operations", "columns": ["mappingid", "co2_reduction"]}], "writes": [{"table": "sponsor_trials", "columns": ["mappingid", "co2_reduction"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO useracct SELECT education_id, building_phone FROM industry_funding WHERE education_id > 128\")\n", "labels": {"reads": [{"table": "industry_funding", "columns": ["education_id", "building_phone"]}], "writes": [{"table": "useracct", "columns": ["education_id", "building_phone"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.stg_clicks_delta SELECT * FROM legacy\ncur.execute(\"SELECT transaction_type_description, author_or_editor FROM aircraft_flights LIMIT 220\")\n", "labels": {"reads": [{"table": "aircraft_flights", "columns": ["transaction_type_description", "author_or_editor"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"bi.bi_member_point\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"safety_data\")\n", "labels": {"reads": [{"table": "bi.bi_member_point", "columns": null}], "writes": [{"table": "safety_data", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"stock\")\nsave_to_output(df, \"expensive_space_missions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stock", "columns": null}], "writes": [{"table": "expensive_space_missions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO geologicalsurvey SELECT * FROM legacy\nspark.sql(\"INSERT INTO autonomousvehicleaccidents SELECT peakhourid, customer_name, injury_date FROM sportsinfo WHERE peakhourid > 242\")\n", "labels": {"reads": [{"table": "sportsinfo", "columns": ["peakhourid", "customer_name", "injury_date"]}], "writes": [{"table": "autonomousvehicleaccidents", "columns": ["peakhourid", "customer_name", "injury_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT team_name, container_count FROM shark_biomass LIMIT 422\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO regulatory_frameworks SELECT artpiecename, famous_title, time_hour FROM market_trends WHERE artpiecename > 17\")\n", "labels": {"reads": [{"table": "shark_biomass", "columns": ["team_name", "container_count"]}, {"table": "market_trends", "columns": ["artpiecename", "famous_title", "time_hour"]}], "writes": [{"table": "regulatory_frameworks", "columns": ["artpiecename", "famous_title", "time_hour"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"climate_data\");\ndf.write().mode(\"overwrite\").saveAsTable(\"participants_in_events\");\n", "labels": {"reads": [{"table": "climate_data", "columns": null}], "writes": [{"table": "participants_in_events", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi_shipments_daily SELECT * FROM legacy\ncur.execute(\"SELECT chemical_name, centername FROM departments LIMIT 477\")\n", "labels": {"reads": [{"table": "departments", "columns": ["chemical_name", "centername"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 343;\nEOF\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["warehouse_id", "oct", "contributionid"]}], "writes": [{"table": "dws_coupon_use_df", "columns": ["warehouse_id", "oct", "contributionid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO waterconservationbudget SELECT title, offender_id FROM feed WHERE title > 431\")\n", "labels": {"reads": [{"table": "feed", "columns": ["title", "offender_id"]}], "writes": [{"table": "waterconservationbudget", "columns": ["title", "offender_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mars_rovers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"nba\")\n", "labels": {"reads": [{"table": "mars_rovers", "columns": null}], "writes": [{"table": "nba", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"acidification_data\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dailystreams\");\n", "labels": {"reads": [{"table": "acidification_data", "columns": null}], "writes": [{"table": "dailystreams", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO prescribes SELECT garment_id, virtual_tour_views FROM co_ownership WHERE garment_id > 133\");\n", "labels": {"reads": [{"table": "co_ownership", "columns": ["garment_id", "virtual_tour_views"]}], "writes": [{"table": "prescribes", "columns": ["garment_id", "virtual_tour_views"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 276;\nSQL\n", "labels": {"reads": [{"table": "mart_events_full", "columns": ["gname", "incorporated_in"]}, {"table": "container", "columns": ["room_count", "claim_status_description", "club_id", "budget_amount"]}], "writes": [{"table": "community.donations", "columns": ["room_count", "claim_status_description", "club_id", "budget_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT quantityproduced, caseid FROM tickets LIMIT 97\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO project_duration SELECT attack_count, mental_health_rating, policyid FROM match WHERE attack_count > 395\")\n", "labels": {"reads": [{"table": "tickets", "columns": ["quantityproduced", "caseid"]}, {"table": "match", "columns": ["attack_count", "mental_health_rating", "policyid"]}], "writes": [{"table": "project_duration", "columns": ["attack_count", "mental_health_rating", "policyid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"scientists\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "scientists", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"player_award\")\nsrc.write.insertInto(\"ods.ods_member_point_df\", overwrite=True)\n", "labels": {"reads": [{"table": "player_award", "columns": null}], "writes": [{"table": "ods.ods_member_point_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO region_stats (damage_millions_usd, num_libraries) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "region_stats", "columns": ["damage_millions_usd", "num_libraries"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO paris_train (type, avg_depth) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "paris_train", "columns": ["type", "avg_depth"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO virtual_tour_engagement SELECT fleet_name, has_spf FROM date WHERE fleet_name > 398\")\n", "labels": {"reads": [{"table": "date", "columns": ["fleet_name", "has_spf"]}], "writes": [{"table": "virtual_tour_engagement", "columns": ["fleet_name", "has_spf"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO vocals SELECT date_of_birth, shipment_date, investment_date, restaurant_name FROM film_actor WHERE date_of_birth > 49\"], check=True)\n", "labels": {"reads": [{"table": "film_actor", "columns": ["date_of_birth", "shipment_date", "investment_date", "restaurant_name"]}], "writes": [{"table": "vocals", "columns": ["date_of_birth", "shipment_date", "investment_date", "restaurant_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO workforce_development_programs SELECT attendanceid, habitat_name FROM stg.stg_users WHERE attendanceid > 360\"\n", "labels": {"reads": [{"table": "stg.stg_users", "columns": ["attendanceid", "habitat_name"]}], "writes": [{"table": "workforce_development_programs", "columns": ["attendanceid", "habitat_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 430;\nEOF\n", "labels": {"reads": [{"table": "dws.dws_events_df", "columns": ["coowner_name", "claim_status_description", "city_area"]}], "writes": [{"table": "taxi_data", "columns": ["coowner_name", "claim_status_description", "city_area"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO dwd_payments_delta SELECT article_id, crop_name, authid, winning_pilot FROM stg.risk_score_di WHERE article_id > 194\");\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": ["article_id", "crop_name", "authid", "winning_pilot"]}], "writes": [{"table": "dwd_payments_delta", "columns": ["article_id", "crop_name", "authid", "winning_pilot"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO address SELECT hub_id, election_cycle, cases FROM classrooms WHERE hub_id > 375\"\n", "labels": {"reads": [{"table": "classrooms", "columns": ["hub_id", "election_cycle", "cases"]}], "writes": [{"table": "address", "columns": ["hub_id", "election_cycle", "cases"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.reviewscore > 433).all()\n# src table: red_line\nengine.execute(\"INSERT INTO habitat SELECT * FROM red_line\")\n", "labels": {"reads": [{"table": "red_line", "columns": null}], "writes": [{"table": "habitat", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_table(ctx, \"healthcare\")\nsave_to_target(df, \"accessible_tech_categories\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "healthcare", "columns": null}], "writes": [{"table": "accessible_tech_categories", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO aquatic_species SELECT saleid, attorney, app_name, devices FROM tourism_activities WHERE saleid > 152\"\n", "labels": {"reads": [{"table": "tourism_activities", "columns": ["saleid", "attorney", "app_name", "devices"]}], "writes": [{"table": "aquatic_species", "columns": ["saleid", "attorney", "app_name", "devices"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO industrial_building_energy_efficiency SELECT birth_date, shipping_agent_code FROM green_energy_lending_programs WHERE birth_date > 289\"\n", "labels": {"reads": [{"table": "green_energy_lending_programs", "columns": ["birth_date", "shipping_agent_code"]}], "writes": [{"table": "industrial_building_energy_efficiency", "columns": ["birth_date", "shipping_agent_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bikerental SELECT seal_species, health_equity_metric_2, home_team_points, nickname FROM operations WHERE seal_species > 272\"], check=True)\n", "labels": {"reads": [{"table": "operations", "columns": ["seal_species", "health_equity_metric_2", "home_team_points", "nickname"]}], "writes": [{"table": "bikerental", "columns": ["seal_species", "health_equity_metric_2", "home_team_points", "nickname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO disinformation_detection (player_api_id, expertise) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "disinformation_detection", "columns": ["player_api_id", "expertise"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT workshop_name, users_engaged FROM mart.mart_campaigns_daily LIMIT 258\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": ["workshop_name", "users_engaged"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO all_programs SELECT subscribe_date, menuitemname, initiative_region, game_count FROM film WHERE subscribe_date > 223\")\n", "labels": {"reads": [{"table": "film", "columns": ["subscribe_date", "menuitemname", "initiative_region", "game_count"]}], "writes": [{"table": "all_programs", "columns": ["subscribe_date", "menuitemname", "initiative_region", "game_count"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mars_missions SELECT a.council_tax_id, b.tour_type FROM mart.mart_products_hourly a JOIN project b ON a.wind_speed_mph = b.wind_speed_mph\"\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": null}, {"table": "project", "columns": null}], "writes": [{"table": "mars_missions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table co2_sequestration --target-dir /tmp/land\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO dw.dw_sessions_full SELECT initiative_name, institution, date_of_notes, incidenttype FROM game_results WHERE initiative_name > 112\");\n", "labels": {"reads": [{"table": "game_results", "columns": ["initiative_name", "institution", "date_of_notes", "incidenttype"]}], "writes": [{"table": "dw.dw_sessions_full", "columns": ["initiative_name", "institution", "date_of_notes", "incidenttype"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dw.dw_events_di --columns date_complaint_raised,crime_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_events_di", "columns": ["date_complaint_raised", "crime_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO carbon_offset_south_america SELECT a.total_value_purchased, b.menucategory FROM mediatype a JOIN government.region b ON a.creation_date = b.creation_date\"\n", "labels": {"reads": [{"table": "mediatype", "columns": null}, {"table": "government.region", "columns": null}], "writes": [{"table": "carbon_offset_south_america", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO healthcare SELECT studentid, instructor_id, range FROM useracct WHERE studentid > 111\"], check=True)\n", "labels": {"reads": [{"table": "useracct", "columns": ["studentid", "instructor_id", "range"]}], "writes": [{"table": "healthcare", "columns": ["studentid", "instructor_id", "range"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT energy_efficiency_rating, gender FROM regional_railways LIMIT 247\")\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO sponsor_trials SELECT recycler_id, city_population FROM manufacturer WHERE recycler_id > 327\")\n", "labels": {"reads": [{"table": "regional_railways", "columns": ["energy_efficiency_rating", "gender"]}, {"table": "manufacturer", "columns": ["recycler_id", "city_population"]}], "writes": [{"table": "sponsor_trials", "columns": ["recycler_id", "city_population"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO daily_articles_by_category SELECT business_name, reservoir_id FROM concert_revenue WHERE business_name > 196\")\n", "labels": {"reads": [{"table": "concert_revenue", "columns": ["business_name", "reservoir_id"]}], "writes": [{"table": "daily_articles_by_category", "columns": ["business_name", "reservoir_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO inspections SELECT max_speed, transaction_type FROM dws.dws_member_point_df WHERE max_speed > 310\");\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": ["max_speed", "transaction_type"]}], "writes": [{"table": "inspections", "columns": ["max_speed", "transaction_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table labor_practices --target-dir /tmp/land\n", "labels": {"reads": [{"table": "labor_practices", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 261;\nEOF\n", "labels": {"reads": [{"table": "government_transparency", "columns": ["silver", "sector"]}], "writes": [{"table": "researchgrants", "columns": ["silver", "sector"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO marketingbudget SELECT wrestler_id, claimdate FROM ods.ods_users_di WHERE wrestler_id > 28\")\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": ["wrestler_id", "claimdate"]}], "writes": [{"table": "marketingbudget", "columns": ["wrestler_id", "claimdate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 164;\nSQL\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": ["productname", "annual_revenue"]}, {"table": "co2_emission", "columns": ["sales_channel", "vice_president_vote"]}], "writes": [{"table": "exhibitionsartworks", "columns": ["sales_channel", "vice_president_vote"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"open_pedagogy_enrollment\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "open_pedagogy_enrollment", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"medical_professionals\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "medical_professionals", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT amount_due, volume FROM na_schema.hospitals LIMIT 21\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "na_schema.hospitals", "columns": ["amount_due", "volume"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO ads.member_point SELECT a.factory, b.workeridentity FROM list a JOIN dwd.dwd_exposure_df b ON a.openingid = b.openingid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "list", "columns": null}, {"table": "dwd.dwd_exposure_df", "columns": null}], "writes": [{"table": "ads.member_point", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO policy_advocacy SELECT * FROM legacy\ncur.execute(\"SELECT item_size, parent_organization_id FROM device_usage LIMIT 184\")\n", "labels": {"reads": [{"table": "device_usage", "columns": ["item_size", "parent_organization_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO virtual_tourism SELECT sensor_id, document_date, recipe_id, no_of_customers FROM socialimpactinvestments WHERE sensor_id > 477\"\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": ["sensor_id", "document_date", "recipe_id", "no_of_customers"]}], "writes": [{"table": "virtual_tourism", "columns": ["sensor_id", "document_date", "recipe_id", "no_of_customers"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart.mart_coupon_use_df\"\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"auto_show\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads.risk_score\")\n", "labels": {"reads": [{"table": "auto_show", "columns": null}], "writes": [{"table": "ads.risk_score", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"green_building_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"atlantic_ocean\")\n", "labels": {"reads": [{"table": "green_building_projects", "columns": null}], "writes": [{"table": "atlantic_ocean", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO zipcodes (cultural_diversity, workername) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "zipcodes", "columns": ["cultural_diversity", "workername"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO maintenancerequests SELECT alid, missions, product_details, avg_usage FROM mines WHERE alid > 467\"\n", "labels": {"reads": [{"table": "mines", "columns": ["alid", "missions", "product_details", "avg_usage"]}], "writes": [{"table": "maintenancerequests", "columns": ["alid", "missions", "product_details", "avg_usage"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"animals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "animals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"catalog_structure\").toPandas()\ndf[[\"author_or_editor\", \"formats\"]].to_sql(\"sustainability_metrics\", engine, index=False)\n", "labels": {"reads": [{"table": "catalog_structure", "columns": null}], "writes": [{"table": "sustainability_metrics", "columns": ["author_or_editor", "formats"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 184;\nEOF\n", "labels": {"reads": [{"table": "wastedata", "columns": ["cuisine_name", "attorney_id"]}], "writes": [{"table": "worker_union", "columns": ["cuisine_name", "attorney_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO renewableenergy SELECT * FROM legacy\nspark.sql(\"INSERT INTO stg.member_point_df SELECT nation, day_number, completion_year, product_color FROM climate_monitoring_stations WHERE nation > 397\")\n", "labels": {"reads": [{"table": "climate_monitoring_stations", "columns": ["nation", "day_number", "completion_year", "product_color"]}], "writes": [{"table": "stg.member_point_df", "columns": ["nation", "day_number", "completion_year", "product_color"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM labor_hours\", conn)\ndf.to_sql(\"rent_arrears\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "labor_hours", "columns": null}], "writes": [{"table": "rent_arrears", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dw.dw_sessions_delta\", conn)\ndf.to_sql(\"freshwaterfinfish\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dw.dw_sessions_delta", "columns": null}], "writes": [{"table": "freshwaterfinfish", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO purchase SELECT sales_billion, host, primaryaffiliation FROM crops_table WHERE sales_billion > 223\"\n", "labels": {"reads": [{"table": "crops_table", "columns": ["sales_billion", "host", "primaryaffiliation"]}], "writes": [{"table": "purchase", "columns": ["sales_billion", "host", "primaryaffiliation"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_life_research_stations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.stg_events_hourly\")\n", "labels": {"reads": [{"table": "marine_life_research_stations", "columns": null}], "writes": [{"table": "stg.stg_events_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.profits_in_billion > 64).all()\n# src table: investment_rounds\nengine.execute(\"INSERT INTO supplier_addresses SELECT * FROM investment_rounds\")\n", "labels": {"reads": [{"table": "investment_rounds", "columns": null}], "writes": [{"table": "supplier_addresses", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"clothingsales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "clothingsales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO regulatory_compliance SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"singer_in_concert\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dancefunding SELECT * FROM legacy\ncur.execute(\"SELECT event_name, review_text FROM permit LIMIT 144\")\n", "labels": {"reads": [{"table": "permit", "columns": ["event_name", "review_text"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT range, characteristic_name FROM shariah_compliant_finance\", engine)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\ndf.to_sql(\"public_schools\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "shariah_compliant_finance", "columns": ["range", "characteristic_name"]}], "writes": [{"table": "public_schools", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 190;\nSQL\n", "labels": {"reads": [{"table": "trends_2022", "columns": ["is_compliant", "investment"]}, {"table": "co2price", "columns": ["company_gender", "receipt_date"]}], "writes": [{"table": "workout_data", "columns": ["company_gender", "receipt_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 19;\nEOF\n", "labels": {"reads": [{"table": "laborstatistics", "columns": ["unit_of_measure", "review_rating", "is_vegan", "volunteer_hours"]}], "writes": [{"table": "baseball_teams", "columns": ["unit_of_measure", "review_rating", "is_vegan", "volunteer_hours"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO fairness_scores SELECT amount_settled, hometeamid, document_type_description FROM market_trends WHERE amount_settled > 338\");\n", "labels": {"reads": [{"table": "market_trends", "columns": ["amount_settled", "hometeamid", "document_type_description"]}], "writes": [{"table": "fairness_scores", "columns": ["amount_settled", "hometeamid", "document_type_description"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"customersregion\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "customersregion", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO store_product SELECT treatment_year, precedent_id, recipe_id FROM state_usage WHERE treatment_year > 21\"\n", "labels": {"reads": [{"table": "state_usage", "columns": ["treatment_year", "precedent_id", "recipe_id"]}], "writes": [{"table": "store_product", "columns": ["treatment_year", "precedent_id", "recipe_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.days_held > 459).all()\n# src table: city_labor_cost\nengine.execute(\"INSERT INTO mart.shipments_delta SELECT * FROM city_labor_cost\")\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": null}], "writes": [{"table": "mart.shipments_delta", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO art_pieces SELECT a.num_fans, b.account_details FROM coffee_prices a JOIN vr_tech b ON a.min_dew_point_f = b.min_dew_point_f\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "coffee_prices", "columns": null}, {"table": "vr_tech", "columns": null}], "writes": [{"table": "art_pieces", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.sessions_full\").toPandas()\ndf[[\"incident_description\", \"address_id\"]].to_sql(\"flights\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.sessions_full", "columns": null}], "writes": [{"table": "flights", "columns": ["incident_description", "address_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"temperaturehistory\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "temperaturehistory", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO media_library SELECT garment_name, court_appearances, quality FROM sustainabilityratings WHERE garment_name > 464\")\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": ["garment_name", "court_appearances", "quality"]}], "writes": [{"table": "media_library", "columns": ["garment_name", "court_appearances", "quality"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO menu_vendors SELECT system_id, number_thousands FROM student_tests_taken WHERE system_id > 485\"], check=True)\n", "labels": {"reads": [{"table": "student_tests_taken", "columns": ["system_id", "number_thousands"]}], "writes": [{"table": "menu_vendors", "columns": ["system_id", "number_thousands"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"trucks\")\npersist_to_sink(df, \"researchpapers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "trucks", "columns": null}], "writes": [{"table": "researchpapers", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.orders SELECT last_used_bus, project_type, plan_type, artifacttype FROM marine_mammals WHERE last_used_bus > 317\"\n", "labels": {"reads": [{"table": "marine_mammals", "columns": ["last_used_bus", "project_type", "plan_type", "artifacttype"]}], "writes": [{"table": "ads.orders", "columns": ["last_used_bus", "project_type", "plan_type", "artifacttype"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ads_users_hourly depends on chemical_processes\ndbt run -s ads_users_hourly --vars '{\"src\":\"chemical_processes\"}'\n", "labels": {"reads": [{"table": "chemical_processes", "columns": null}], "writes": [{"table": "ads_users_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"humanitarian_aid\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"australia_offset_programs\")\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": null}], "writes": [{"table": "australia_offset_programs", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO busmaintenance SELECT phase, attraction_id, years_operating FROM waste_generation WHERE phase > 260\"\n", "labels": {"reads": [{"table": "waste_generation", "columns": ["phase", "attraction_id", "years_operating"]}], "writes": [{"table": "busmaintenance", "columns": ["phase", "attraction_id", "years_operating"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO green_buildings_us SELECT * FROM legacy\nspark.sql(\"INSERT INTO bi.events_df SELECT astronautid, num_attendees FROM cargo_data WHERE astronautid > 490\")\n", "labels": {"reads": [{"table": "cargo_data", "columns": ["astronautid", "num_attendees"]}], "writes": [{"table": "bi.events_df", "columns": ["astronautid", "num_attendees"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"canals\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "canals", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.energy_id > 492).all()\n# src table: communityengagementmetrics\nengine.execute(\"INSERT INTO red_line SELECT * FROM communityengagementmetrics\")\n", "labels": {"reads": [{"table": "communityengagementmetrics", "columns": null}], "writes": [{"table": "red_line", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table hotel_ratings --columns capacity_percentage,role_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "hotel_ratings", "columns": ["capacity_percentage", "role_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dwd.products_di SELECT yield_id, fund_name FROM urbanagricrop WHERE yield_id > 403\"\n", "labels": {"reads": [{"table": "urbanagricrop", "columns": ["yield_id", "fund_name"]}], "writes": [{"table": "dwd.products_di", "columns": ["yield_id", "fund_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"product_characteristics\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "product_characteristics", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd_sessions_hourly\").toPandas()\ndf[[\"transit_passengers\", \"instrument\"]].to_sql(\"instructor\", engine, index=False)\n", "labels": {"reads": [{"table": "dwd_sessions_hourly", "columns": null}], "writes": [{"table": "instructor", "columns": ["transit_passengers", "instrument"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT medicine_id, disability_type FROM recall_reports LIMIT 27\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "recall_reports", "columns": ["medicine_id", "disability_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.mart_risk_score_hourly SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO rooms SELECT investor_name, billid, no_of_loans FROM stg.risk_score_hourly WHERE investor_name > 84\")\n", "labels": {"reads": [{"table": "stg.risk_score_hourly", "columns": ["investor_name", "billid", "no_of_loans"]}], "writes": [{"table": "rooms", "columns": ["investor_name", "billid", "no_of_loans"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_table(ctx, \"ods.ods_events_daily\")\nupsert_to_warehouse(df, \"waterconservationinitiatives\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods.ods_events_daily", "columns": null}], "writes": [{"table": "waterconservationinitiatives", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO first_notification_of_loss SELECT a.species_name, b.route_id FROM geneva_motor_show a JOIN communitydevelopment b ON a.don_name = b.don_name\"\n", "labels": {"reads": [{"table": "geneva_motor_show", "columns": null}, {"table": "communitydevelopment", "columns": null}], "writes": [{"table": "first_notification_of_loss", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT awards, building_short_name FROM mining_operation_data LIMIT 500\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "mining_operation_data", "columns": ["awards", "building_short_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"worker_scores\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"technician\")\n", "labels": {"reads": [{"table": "worker_scores", "columns": null}], "writes": [{"table": "technician", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO community_programs SELECT fname, negative FROM operations WHERE fname > 423\"\n", "labels": {"reads": [{"table": "operations", "columns": ["fname", "negative"]}], "writes": [{"table": "community_programs", "columns": ["fname", "negative"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM trainmaintenance\", conn)\ndf.to_sql(\"energy_efficiency_programs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "trainmaintenance", "columns": null}], "writes": [{"table": "energy_efficiency_programs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO humanitarian_aid SELECT union_members, loadingstart, booking_end_date, trainingtitle FROM service_budget WHERE union_members > 271\"\n", "labels": {"reads": [{"table": "service_budget", "columns": ["union_members", "loadingstart", "booking_end_date", "trainingtitle"]}], "writes": [{"table": "humanitarian_aid", "columns": ["union_members", "loadingstart", "booking_end_date", "trainingtitle"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO india_solar_power SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artist_data (emp_dob, date_contact_to) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artist_data", "columns": ["emp_dob", "date_contact_to"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO trainmaintenance SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO articles_es SELECT habitat, bridgeid, sale_id FROM ads.ads_payments WHERE habitat > 236\"\n", "labels": {"reads": [{"table": "ads.ads_payments", "columns": ["habitat", "bridgeid", "sale_id"]}], "writes": [{"table": "articles_es", "columns": ["habitat", "bridgeid", "sale_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO virtual_tour_revenue SELECT employeeid, maintenance_type, products_this_year FROM royal_family WHERE employeeid > 23\"\n", "labels": {"reads": [{"table": "royal_family", "columns": ["employeeid", "maintenance_type", "products_this_year"]}], "writes": [{"table": "virtual_tour_revenue", "columns": ["employeeid", "maintenance_type", "products_this_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO economic_diversification_argentina SELECT sport, patient_count, observation_id, yield_per_acre FROM domesticconferences WHERE sport > 354\"], check=True)\n", "labels": {"reads": [{"table": "domesticconferences", "columns": ["sport", "patient_count", "observation_id", "yield_per_acre"]}], "writes": [{"table": "economic_diversification_argentina", "columns": ["sport", "patient_count", "observation_id", "yield_per_acre"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model posts depends on light_rail_lines\ndbt run -s posts --vars 'source: light_rail_lines'\n", "labels": {"reads": [{"table": "light_rail_lines", "columns": null}], "writes": [{"table": "posts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO seafood SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO strategies SELECT * FROM legacy\nspark.sql(\"INSERT INTO nasa_mars_program SELECT height_feet, price_range, vaccination_status, fine FROM wholesale_orders WHERE height_feet > 313\")\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": ["height_feet", "price_range", "vaccination_status", "fine"]}], "writes": [{"table": "nasa_mars_program", "columns": ["height_feet", "price_range", "vaccination_status", "fine"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model carbon_offset_initiatives depends on co2_emissions\ndbt build -s carbon_offset_initiatives --vars '{\"src\":\"co2_emissions\"}'\n", "labels": {"reads": [{"table": "co2_emissions", "columns": null}], "writes": [{"table": "carbon_offset_initiatives", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ai_ethics_policies SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO gamesessions SELECT courtname, address_details, trainingdate, composer FROM coralreefs WHERE courtname > 372\"\n", "labels": {"reads": [{"table": "coralreefs", "columns": ["courtname", "address_details", "trainingdate", "composer"]}], "writes": [{"table": "gamesessions", "columns": ["courtname", "address_details", "trainingdate", "composer"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO community_centers SELECT production_qty, budget_in_billions, inspection_date FROM mart.mart_products_hourly WHERE production_qty > 184\")\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": ["production_qty", "budget_in_billions", "inspection_date"]}], "writes": [{"table": "community_centers", "columns": ["production_qty", "budget_in_billions", "inspection_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT thefttypeid, destination_id FROM livestock\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"ancient_artifacts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "livestock", "columns": ["thefttypeid", "destination_id"]}], "writes": [{"table": "ancient_artifacts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"training\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"restorative_justice_3\")\n", "labels": {"reads": [{"table": "training", "columns": null}], "writes": [{"table": "restorative_justice_3", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO farm (industry_4_0, trader_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "farm", "columns": ["industry_4_0", "trader_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 299;\nSQL\n", "labels": {"reads": [{"table": "smart_contracts", "columns": ["award", "hometeamid"]}, {"table": "stg.stg_campaigns", "columns": ["address_line_1", "opponent_id", "hispanic", "volunteer_name"]}], "writes": [{"table": "organisations", "columns": ["address_line_1", "opponent_id", "hispanic", "volunteer_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"drug_approvals\")\nsrc.write.insertInto(\"bi.bi_sessions_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "drug_approvals", "columns": null}], "writes": [{"table": "bi.bi_sessions_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT start_therapy, farm_id FROM sustainableproduction LIMIT 120\")\nimport logging\nspark.sql(\"INSERT INTO low_value_contracts SELECT daily_sales, country_name, volunteerid, application_date FROM disasters WHERE daily_sales > 371\")\n", "labels": {"reads": [{"table": "sustainableproduction", "columns": ["start_therapy", "farm_id"]}, {"table": "disasters", "columns": ["daily_sales", "country_name", "volunteerid", "application_date"]}], "writes": [{"table": "low_value_contracts", "columns": ["daily_sales", "country_name", "volunteerid", "application_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO investors SELECT a.log_entry_date, b.detention_summary FROM viewership a JOIN festivals b ON a.policy_name = b.policy_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "viewership", "columns": null}, {"table": "festivals", "columns": null}], "writes": [{"table": "investors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model cargo_data depends on program_outcomes\ndbt run --models cargo_data --vars '{\"src\":\"program_outcomes\"}'\n", "labels": {"reads": [{"table": "program_outcomes", "columns": null}], "writes": [{"table": "cargo_data", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bioprocesses SELECT donorid, dispensaryid FROM reporters WHERE donorid > 294\"\n", "labels": {"reads": [{"table": "reporters", "columns": ["donorid", "dispensaryid"]}], "writes": [{"table": "bioprocesses", "columns": ["donorid", "dispensaryid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table electricvehicles --columns allocation_date,clean_jerk --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "electricvehicles", "columns": ["allocation_date", "clean_jerk"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO animal_rehab SELECT a.mental_health_status, b.high_temperature FROM midwest_materials a JOIN carbon_offset_projects b ON a.order_date = b.order_date\"\n", "labels": {"reads": [{"table": "midwest_materials", "columns": null}, {"table": "carbon_offset_projects", "columns": null}], "writes": [{"table": "animal_rehab", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicle_safety_testing\").toPandas()\ndf[[\"age\", \"consider_rate\"]].to_sql(\"urban_farms\", engine, index=False)\n", "labels": {"reads": [{"table": "vehicle_safety_testing", "columns": null}], "writes": [{"table": "urban_farms", "columns": ["age", "consider_rate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO tvshows SELECT course_name, policy_holder_id FROM automation_tech WHERE course_name > 411\"\n", "labels": {"reads": [{"table": "automation_tech", "columns": ["course_name", "policy_holder_id"]}], "writes": [{"table": "tvshows", "columns": ["course_name", "policy_holder_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO armed_forces SELECT costid, astronautid FROM concert_events WHERE costid > 492\"\n", "labels": {"reads": [{"table": "concert_events", "columns": ["costid", "astronautid"]}], "writes": [{"table": "armed_forces", "columns": ["costid", "astronautid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model participants depends on digital_assets\ndbt build --select participants --vars '{\"source_table\":\"digital_assets\"}'\n", "labels": {"reads": [{"table": "digital_assets", "columns": null}], "writes": [{"table": "participants", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO teachers SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"band\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "band", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO research_vessels SELECT * FROM legacy\nspark.sql(\"INSERT INTO browser SELECT contact_staff_id, client_name, garmentid, team_name FROM goals WHERE contact_staff_id > 11\")\n", "labels": {"reads": [{"table": "goals", "columns": ["contact_staff_id", "client_name", "garmentid", "team_name"]}], "writes": [{"table": "browser", "columns": ["contact_staff_id", "client_name", "garmentid", "team_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT uses_vr, safety_id FROM tourism LIMIT 53\")\nimport logging\nspark.sql(\"INSERT INTO elimination SELECT daily_sales, actor_name, objectnumber, province_name FROM financial_capability WHERE daily_sales > 201\")\n", "labels": {"reads": [{"table": "tourism", "columns": ["uses_vr", "safety_id"]}, {"table": "financial_capability", "columns": ["daily_sales", "actor_name", "objectnumber", "province_name"]}], "writes": [{"table": "elimination", "columns": ["daily_sales", "actor_name", "objectnumber", "province_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO circuits SELECT gamepreference, game_genre, carrier, hourlyrate FROM virtual_tourism WHERE gamepreference > 93\"\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": ["gamepreference", "game_genre", "carrier", "hourlyrate"]}], "writes": [{"table": "circuits", "columns": ["gamepreference", "game_genre", "carrier", "hourlyrate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT factory_id, hometeamid FROM bi.bi_inventory_di LIMIT 499\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO restaurants_tx SELECT date_from, hospitalid, year_opened FROM classroom WHERE date_from > 419\")\n", "labels": {"reads": [{"table": "bi.bi_inventory_di", "columns": ["factory_id", "hometeamid"]}, {"table": "classroom", "columns": ["date_from", "hospitalid", "year_opened"]}], "writes": [{"table": "restaurants_tx", "columns": ["date_from", "hospitalid", "year_opened"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO underwater_cables SELECT number_of_matches, production_qty FROM upgrades WHERE number_of_matches > 325\"\n", "labels": {"reads": [{"table": "upgrades", "columns": ["number_of_matches", "production_qty"]}], "writes": [{"table": "underwater_cables", "columns": ["number_of_matches", "production_qty"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 1;\nSQL\n", "labels": {"reads": [{"table": "patienttreatments", "columns": ["festival_id", "num_workers"]}, {"table": "menu", "columns": ["cruelty_free", "store_name", "quantity_sold", "sport_id"]}], "writes": [{"table": "genetics_stats.research_projects", "columns": ["cruelty_free", "store_name", "quantity_sold", "sport_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dw.dw_users_di SELECT * FROM legacy\ncur.execute(\"SELECT postalcode, offender_id FROM equipment_sales LIMIT 238\")\n", "labels": {"reads": [{"table": "equipment_sales", "columns": ["postalcode", "offender_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"performance_scores\").where(\"dt = current_date()\").writeTo(\"head\").append()\n", "labels": {"reads": [{"table": "performance_scores", "columns": null}], "writes": [{"table": "head", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO savings_programs SELECT payment_id, activity_name FROM rigs WHERE payment_id > 130\");\n", "labels": {"reads": [{"table": "rigs", "columns": ["payment_id", "activity_name"]}], "writes": [{"table": "savings_programs", "columns": ["payment_id", "activity_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model artcontributors depends on college\ndbt build -s artcontributors --vars '{\"src\":\"college\"}'\n", "labels": {"reads": [{"table": "college", "columns": null}], "writes": [{"table": "artcontributors", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.painting_name > 307).all()\n# src table: bi.events_df\nengine.execute(\"INSERT INTO rural_projects SELECT * FROM bi.events_df\")\n", "labels": {"reads": [{"table": "bi.events_df", "columns": null}], "writes": [{"table": "rural_projects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM open_pedagogy_enrollment\"\n", "labels": {"reads": [{"table": "open_pedagogy_enrollment", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO evidence_based_policies (resident_id, grade) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "evidence_based_policies", "columns": ["resident_id", "grade"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT classtype, delivery_id FROM military_personnel_africa LIMIT 194\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "military_personnel_africa", "columns": ["classtype", "delivery_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO military_sales SELECT * FROM legacy\ncur.execute(\"SELECT lesson_status_code, vesselid FROM dwd.exposure_hourly LIMIT 105\")\n", "labels": {"reads": [{"table": "dwd.exposure_hourly", "columns": ["lesson_status_code", "vesselid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT funding_received, fan_age FROM bike_share LIMIT 477\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO event SELECT allergytype, artist_gender FROM manufacturersustainability WHERE allergytype > 435\")\n", "labels": {"reads": [{"table": "bike_share", "columns": ["funding_received", "fan_age"]}, {"table": "manufacturersustainability", "columns": ["allergytype", "artist_gender"]}], "writes": [{"table": "event", "columns": ["allergytype", "artist_gender"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 94;\nSQL\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": ["calories", "inclusivehousing"]}, {"table": "fertilizer_usage", "columns": ["ycard", "payment_type_code"]}], "writes": [{"table": "dws.dws_coupon_use_di", "columns": ["ycard", "payment_type_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table student_addresses --columns feedid,vegetable --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "student_addresses", "columns": ["feedid", "vegetable"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO brandrevenue SELECT * FROM legacy\nspark.sql(\"INSERT INTO certificate SELECT usage, cause_id, donor_category FROM container WHERE usage > 152\")\n", "labels": {"reads": [{"table": "container", "columns": ["usage", "cause_id", "donor_category"]}], "writes": [{"table": "certificate", "columns": ["usage", "cause_id", "donor_category"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO exhibitionsartworks (speed, trips) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "exhibitionsartworks", "columns": ["speed", "trips"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 46;\nSQL\n", "labels": {"reads": [{"table": "gameplatforms", "columns": ["color", "stuid"]}, {"table": "clinic_2022", "columns": ["volunteerdate", "low_temperature", "retailer", "complaint_id"]}], "writes": [{"table": "diplomacy_events", "columns": ["volunteerdate", "low_temperature", "retailer", "complaint_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT end_date, neighborhood FROM dw.dw_coupon_use_daily LIMIT 484\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO accounts SELECT crime_id, itemname, max_salary FROM fruitimport WHERE crime_id > 119\")\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": ["end_date", "neighborhood"]}, {"table": "fruitimport", "columns": ["crime_id", "itemname", "max_salary"]}], "writes": [{"table": "accounts", "columns": ["crime_id", "itemname", "max_salary"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"carbon_prices_3\");\ndf.write().mode(\"overwrite\").saveAsTable(\"game_results\");\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": null}], "writes": [{"table": "game_results", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO cybersecuritybudget SELECT * FROM legacy\nspark.sql(\"INSERT INTO site SELECT domestic_passengers, recycler_id FROM musical WHERE domestic_passengers > 255\")\n", "labels": {"reads": [{"table": "musical", "columns": ["domestic_passengers", "recycler_id"]}], "writes": [{"table": "site", "columns": ["domestic_passengers", "recycler_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"global_tournament\").where(\"dt = current_date()\").writeTo(\"mart.mart_risk_score_hourly\").append()\n", "labels": {"reads": [{"table": "global_tournament", "columns": null}], "writes": [{"table": "mart.mart_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"hockey_players\")\nwrite_to_output(df, \"community_development_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "hockey_players", "columns": null}], "writes": [{"table": "community_development_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.problem_id > 93).all()\n# src table: global_sales_2022\nengine.execute(\"INSERT INTO wind_projects SELECT * FROM global_sales_2022\")\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}], "writes": [{"table": "wind_projects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model innovation_trends depends on fan_purchases\ndbt run --select innovation_trends --vars 'source: fan_purchases'\n", "labels": {"reads": [{"table": "fan_purchases", "columns": null}], "writes": [{"table": "innovation_trends", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO fuel_consumption SELECT recruitername, result, explainability_score FROM unionmembers WHERE recruitername > 248\");\n", "labels": {"reads": [{"table": "unionmembers", "columns": ["recruitername", "result", "explainability_score"]}], "writes": [{"table": "fuel_consumption", "columns": ["recruitername", "result", "explainability_score"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"co_ownership_program\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dws.dws_risk_score_daily\");\n", "labels": {"reads": [{"table": "co_ownership_program", "columns": null}], "writes": [{"table": "dws.dws_risk_score_daily", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT region_name, participation_id FROM artifacts\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"basketball_match\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "artifacts", "columns": ["region_name", "participation_id"]}], "writes": [{"table": "basketball_match", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"playergamehistory\").where(\"dt = current_date()\").writeTo(\"bi.bi_risk_score_df\").append()\n", "labels": {"reads": [{"table": "playergamehistory", "columns": null}], "writes": [{"table": "bi.bi_risk_score_df", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO educationprograms SELECT era, host_city, supplier_company_id, nickname FROM ai_ethics WHERE era > 387\"\n", "labels": {"reads": [{"table": "ai_ethics", "columns": ["era", "host_city", "supplier_company_id", "nickname"]}], "writes": [{"table": "educationprograms", "columns": ["era", "host_city", "supplier_company_id", "nickname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sport, incident_type_description FROM healthcare_centers\", engine)\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"dysprosiumproduction\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": ["sport", "incident_type_description"]}], "writes": [{"table": "dysprosiumproduction", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 139;\nEOF\n", "labels": {"reads": [{"table": "construction_labor", "columns": ["amount_of_transaction", "emp_id", "carbon_offset_tons"]}], "writes": [{"table": "fairness_scores", "columns": ["amount_of_transaction", "emp_id", "carbon_offset_tons"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.ads_cart_item_hourly SELECT defense_contractor_id, billingamount FROM ocean WHERE defense_contractor_id > 243\"\n", "labels": {"reads": [{"table": "ocean", "columns": ["defense_contractor_id", "billingamount"]}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": ["defense_contractor_id", "billingamount"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO military_tech SELECT number_of_vessels, province_name, sentence_id, contractorid FROM dws.shipments_daily WHERE number_of_vessels > 61\")\n", "labels": {"reads": [{"table": "dws.shipments_daily", "columns": ["number_of_vessels", "province_name", "sentence_id", "contractorid"]}], "writes": [{"table": "military_tech", "columns": ["number_of_vessels", "province_name", "sentence_id", "contractorid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO gardens SELECT retailer_name, incorporated_in FROM agricultural_projects WHERE retailer_name > 224\"\n", "labels": {"reads": [{"table": "agricultural_projects", "columns": ["retailer_name", "incorporated_in"]}], "writes": [{"table": "gardens", "columns": ["retailer_name", "incorporated_in"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT routename, join_year FROM mart.mart_users\", engine)\nif not rows:\n logger.warning('empty result')\nimport logging\ndf.to_sql(\"livestock\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_users", "columns": ["routename", "join_year"]}], "writes": [{"table": "livestock", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.dws_events_df SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nsql = \"INSERT INTO opioid_overdoses SELECT a.date_payment_made, b.build_year FROM payments a JOIN fashion_trend_data b ON a.uses_vr = b.uses_vr\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "payments", "columns": null}, {"table": "fashion_trend_data", "columns": null}], "writes": [{"table": "opioid_overdoses", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT mealid, diet FROM dws.events LIMIT 187\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "dws.events", "columns": ["mealid", "diet"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO dws.coupon_use_di SELECT family_name, exhibitionname FROM film_market_estimation WHERE family_name > 200\")\n", "labels": {"reads": [{"table": "film_market_estimation", "columns": ["family_name", "exhibitionname"]}], "writes": [{"table": "dws.coupon_use_di", "columns": ["family_name", "exhibitionname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.wind_speed_mph > 407).all()\n# src table: smartcitycosts\nengine.execute(\"INSERT INTO ref_product_categories SELECT * FROM smartcitycosts\")\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": null}], "writes": [{"table": "ref_product_categories", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM permit\", conn)\ndf.to_sql(\"rehab_centers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "permit", "columns": null}], "writes": [{"table": "rehab_centers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO attendee_demographics SELECT a.production_mwh, b.co2_reduction_tons FROM disaster_response_donations a JOIN farm_competition b ON a.authid = b.authid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": null}, {"table": "farm_competition", "columns": null}], "writes": [{"table": "attendee_demographics", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"waste_types\").toPandas()\ndf[[\"sessionid\", \"releasedate\"]].to_sql(\"collections\", engine, index=False)\n", "labels": {"reads": [{"table": "waste_types", "columns": null}], "writes": [{"table": "collections", "columns": ["sessionid", "releasedate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi_shipments_daily SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws.dws_shipments_full\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"project_timeline\")\n", "labels": {"reads": [{"table": "dws.dws_shipments_full", "columns": null}], "writes": [{"table": "project_timeline", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi.bi_payments_full (eia_date, port_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_payments_full", "columns": ["eia_date", "port_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO perpetrator SELECT date_formed, local_authority, state_county FROM deep_sea_species WHERE date_formed > 486\");\n", "labels": {"reads": [{"table": "deep_sea_species", "columns": ["date_formed", "local_authority", "state_county"]}], "writes": [{"table": "perpetrator", "columns": ["date_formed", "local_authority", "state_county"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.orders_daily\"\n", "labels": {"reads": [{"table": "stg.orders_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"culturalcompetencytraining\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dispensaries\")\n", "labels": {"reads": [{"table": "culturalcompetencytraining", "columns": null}], "writes": [{"table": "dispensaries", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT zone, mid FROM ods_sessions LIMIT 173\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "ods_sessions", "columns": ["zone", "mid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dw.dw_campaigns_di SELECT startdate, carrierid FROM eia_schedule WHERE startdate > 483\"], check=True)\n", "labels": {"reads": [{"table": "eia_schedule", "columns": ["startdate", "carrierid"]}], "writes": [{"table": "dw.dw_campaigns_di", "columns": ["startdate", "carrierid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"participants\").where(\"dt = current_date()\").writeTo(\"battery_projects\").append()\n", "labels": {"reads": [{"table": "participants", "columns": null}], "writes": [{"table": "battery_projects", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO sales_2 SELECT dispensary_id, projecttype FROM aid_missions WHERE dispensary_id > 364\")\n", "labels": {"reads": [{"table": "aid_missions", "columns": ["dispensary_id", "projecttype"]}], "writes": [{"table": "sales_2", "columns": ["dispensary_id", "projecttype"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 32;\nEOF\n", "labels": {"reads": [{"table": "arctictemperature", "columns": ["donationid", "enable_dm"]}], "writes": [{"table": "state_contracts", "columns": ["donationid", "enable_dm"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO farmers SELECT a.labordate, b.centerid FROM film_category a JOIN trainingprograms b ON a.meal_date = b.meal_date\"\n", "labels": {"reads": [{"table": "film_category", "columns": null}, {"table": "trainingprograms", "columns": null}], "writes": [{"table": "farmers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table tour_guides --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tour_guides", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ticket_sales\").where(\"dt = current_date()\").writeTo(\"technician\").append()\n", "labels": {"reads": [{"table": "ticket_sales", "columns": null}], "writes": [{"table": "technician", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO government_transparency (cell_mobile_phone_number, sector_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "government_transparency", "columns": ["cell_mobile_phone_number", "sector_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO program_budget SELECT policy_holder_id, event_name FROM artistsdemographics WHERE policy_holder_id > 128\"\n", "labels": {"reads": [{"table": "artistsdemographics", "columns": ["policy_holder_id", "event_name"]}], "writes": [{"table": "program_budget", "columns": ["policy_holder_id", "event_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO city SELECT share_in_percent, cinema_id, haslegalprecedent, vrdevice FROM province.human_rights_data WHERE share_in_percent > 4\")\n", "labels": {"reads": [{"table": "province.human_rights_data", "columns": ["share_in_percent", "cinema_id", "haslegalprecedent", "vrdevice"]}], "writes": [{"table": "city", "columns": ["share_in_percent", "cinema_id", "haslegalprecedent", "vrdevice"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO immunization SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"russia_nato_diplomacy\")\nsrc.write.insertInto(\"mart.mart_device_log\", overwrite=True)\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": null}], "writes": [{"table": "mart.mart_device_log", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO cyber_incidents SELECT host_city, vessel_id, animal_species, awards FROM film WHERE host_city > 36\")\n", "labels": {"reads": [{"table": "film", "columns": ["host_city", "vessel_id", "animal_species", "awards"]}], "writes": [{"table": "cyber_incidents", "columns": ["host_city", "vessel_id", "animal_species", "awards"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stock SELECT athleteid, updated_at, transaction_value FROM technology_access WHERE athleteid > 174\"\n", "labels": {"reads": [{"table": "technology_access", "columns": ["athleteid", "updated_at", "transaction_value"]}], "writes": [{"table": "stock", "columns": ["athleteid", "updated_at", "transaction_value"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO passenger_trips (department, mid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "passenger_trips", "columns": ["department", "mid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 47;\nEOF\n", "labels": {"reads": [{"table": "activities", "columns": ["model_id", "cultivatorname", "extraction_amount"]}], "writes": [{"table": "countries", "columns": ["model_id", "cultivatorname", "extraction_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT severity, threats FROM restorative_justice_programs LIMIT 157\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO iron SELECT animal_type, contributor FROM recyclingratessouthamerica WHERE animal_type > 382\")\n", "labels": {"reads": [{"table": "restorative_justice_programs", "columns": ["severity", "threats"]}, {"table": "recyclingratessouthamerica", "columns": ["animal_type", "contributor"]}], "writes": [{"table": "iron", "columns": ["animal_type", "contributor"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO fabricinventory (operation_count, document_status_description) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "fabricinventory", "columns": ["operation_count", "document_status_description"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT session_name, spacecraft_id FROM opendatainitiatives LIMIT 269\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "opendatainitiatives", "columns": ["session_name", "spacecraft_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO highest_scores SELECT billing, swimmer_id FROM ocean_acidification_antarctic WHERE billing > 114\"], check=True)\n", "labels": {"reads": [{"table": "ocean_acidification_antarctic", "columns": ["billing", "swimmer_id"]}], "writes": [{"table": "highest_scores", "columns": ["billing", "swimmer_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO weapons SELECT * FROM legacy\ncur.execute(\"SELECT pieces, asessment_outcome_code FROM school LIMIT 242\")\n", "labels": {"reads": [{"table": "school", "columns": ["pieces", "asessment_outcome_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"genetics.experiments\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"feedback\")\n", "labels": {"reads": [{"table": "genetics.experiments", "columns": null}], "writes": [{"table": "feedback", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dw.dw_member_point_di\", conn)\ndf.to_sql(\"loan\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dw.dw_member_point_di", "columns": null}], "writes": [{"table": "loan", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO developers SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.cb_year > 6).all()\n# src table: professionals\nengine.execute(\"INSERT INTO voting_data SELECT * FROM professionals\")\n", "labels": {"reads": [{"table": "professionals", "columns": null}], "writes": [{"table": "voting_data", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO malicious_activity SELECT investment_id, clubdesc FROM companies_extended WHERE investment_id > 89\"\n", "labels": {"reads": [{"table": "companies_extended", "columns": ["investment_id", "clubdesc"]}], "writes": [{"table": "malicious_activity", "columns": ["investment_id", "clubdesc"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO ads_sessions_di SELECT research_id, purchases FROM crops_table WHERE research_id > 196\")\n", "labels": {"reads": [{"table": "crops_table", "columns": ["research_id", "purchases"]}], "writes": [{"table": "ads_sessions_di", "columns": ["research_id", "purchases"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"renewable_energy_investments\")\nsrc.write.insertInto(\"labor_statistics\", overwrite=True)\n", "labels": {"reads": [{"table": "renewable_energy_investments", "columns": null}], "writes": [{"table": "labor_statistics", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO voting_record SELECT width, games FROM aircraft_flights WHERE width > 446\");\n", "labels": {"reads": [{"table": "aircraft_flights", "columns": ["width", "games"]}], "writes": [{"table": "voting_record", "columns": ["width", "games"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cuisine, organized_by FROM fish_farms LIMIT 222\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO local_impact_japan SELECT billing_city, casestatus FROM ytterbiumproduction WHERE billing_city > 245\")\n", "labels": {"reads": [{"table": "fish_farms", "columns": ["cuisine", "organized_by"]}, {"table": "ytterbiumproduction", "columns": ["billing_city", "casestatus"]}], "writes": [{"table": "local_impact_japan", "columns": ["billing_city", "casestatus"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO wind_energy_projects SELECT founded, laborproductivity FROM movie_ratings WHERE founded > 138\"\n", "labels": {"reads": [{"table": "movie_ratings", "columns": ["founded", "laborproductivity"]}], "writes": [{"table": "wind_energy_projects", "columns": ["founded", "laborproductivity"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods.ods_events_daily SELECT projecttype, attraction_name, semester, individual_last_name FROM adaptation_projects WHERE projecttype > 338\"\n", "labels": {"reads": [{"table": "adaptation_projects", "columns": ["projecttype", "attraction_name", "semester", "individual_last_name"]}], "writes": [{"table": "ods.ods_events_daily", "columns": ["projecttype", "attraction_name", "semester", "individual_last_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"organizations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ai_ethics_policies\")\n", "labels": {"reads": [{"table": "organizations", "columns": null}], "writes": [{"table": "ai_ethics_policies", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"budget_allocations\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dwd.dwd_orders_daily\")\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "dwd.dwd_orders_daily", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO stg.stg_products_delta SELECT a.artist_id, b.donorgender FROM colorado_river_basin a JOIN ads.ads_users_hourly b ON a.account_name = b.account_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "colorado_river_basin", "columns": null}, {"table": "ads.ads_users_hourly", "columns": null}], "writes": [{"table": "stg.stg_products_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO department_publications SELECT a.screening_id, b.clubid FROM humanitarian_operations a JOIN purchases b ON a.order_date = b.order_date\"\n", "labels": {"reads": [{"table": "humanitarian_operations", "columns": null}, {"table": "purchases", "columns": null}], "writes": [{"table": "department_publications", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO community_policing_events (athlete_name, courtid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "community_policing_events", "columns": ["athlete_name", "courtid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO assessment_notes SELECT * FROM legacy\nspark.sql(\"INSERT INTO dws_clicks_di SELECT daily_hire_cost, stocking_density, orgid, meter_200 FROM professional_development WHERE daily_hire_cost > 429\")\n", "labels": {"reads": [{"table": "professional_development", "columns": ["daily_hire_cost", "stocking_density", "orgid", "meter_200"]}], "writes": [{"table": "dws_clicks_di", "columns": ["daily_hire_cost", "stocking_density", "orgid", "meter_200"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws.dws_member_point_di\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"fish_purchases\")\n", "labels": {"reads": [{"table": "dws.dws_member_point_di", "columns": null}], "writes": [{"table": "fish_purchases", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO testtypes SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO artist_info SELECT police_force, oct, effort, loan_id FROM waterconservation WHERE police_force > 459\"\n", "labels": {"reads": [{"table": "waterconservation", "columns": ["police_force", "oct", "effort", "loan_id"]}], "writes": [{"table": "artist_info", "columns": ["police_force", "oct", "effort", "loan_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM students_enrollment\", conn)\ndf.to_sql(\"mart.mart_users_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "students_enrollment", "columns": null}], "writes": [{"table": "mart.mart_users_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO stg.users SELECT * FROM legacy\nspark.sql(\"INSERT INTO dwd.dwd_users_hourly SELECT contract_number, spill_name FROM round WHERE contract_number > 19\")\n", "labels": {"reads": [{"table": "round", "columns": ["contract_number", "spill_name"]}], "writes": [{"table": "dwd.dwd_users_hourly", "columns": ["contract_number", "spill_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO airportdata SELECT initiative_type, company_id FROM forests WHERE initiative_type > 453\")\n", "labels": {"reads": [{"table": "forests", "columns": ["initiative_type", "company_id"]}], "writes": [{"table": "airportdata", "columns": ["initiative_type", "company_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO genetics.experiments SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM incident_region\", conn)\ndf.to_sql(\"exit_strategy\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "incident_region", "columns": null}], "writes": [{"table": "exit_strategy", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"training_programs\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "training_programs", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO grapes SELECT part_name, host_country, end_time, inclusivehousing FROM community.donations WHERE part_name > 347\")\n", "labels": {"reads": [{"table": "community.donations", "columns": ["part_name", "host_country", "end_time", "inclusivehousing"]}], "writes": [{"table": "grapes", "columns": ["part_name", "host_country", "end_time", "inclusivehousing"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM flight_safety\", conn)\ndf.to_sql(\"public_works_projects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": [{"table": "public_works_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO atlantic_ocean SELECT show_times_per_day, sustainability_rating FROM candidates WHERE show_times_per_day > 103\")\n", "labels": {"reads": [{"table": "candidates", "columns": ["show_times_per_day", "sustainability_rating"]}], "writes": [{"table": "atlantic_ocean", "columns": ["show_times_per_day", "sustainability_rating"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 330;\nEOF\n", "labels": {"reads": [{"table": "check_ins", "columns": ["facid", "cinema_id", "ratingdate"]}], "writes": [{"table": "ads.refunds", "columns": ["facid", "cinema_id", "ratingdate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO incident SELECT a.customer_details, b.dept_store_chain_name FROM apac_hotel_views a JOIN spacemissions b ON a.marketing_region_descriptrion = b.marketing_region_descriptrion\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": null}, {"table": "spacemissions", "columns": null}], "writes": [{"table": "incident", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO state_usage (oct, pages_per_minute_color) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "state_usage", "columns": ["oct", "pages_per_minute_color"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"attack_outcomes\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"timbersales\")\n", "labels": {"reads": [{"table": "attack_outcomes", "columns": null}], "writes": [{"table": "timbersales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"postseason\").where(\"dt = current_date()\").writeTo(\"permit\").append()\n", "labels": {"reads": [{"table": "postseason", "columns": null}], "writes": [{"table": "permit", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO org_donation SELECT tonnage, marketing_region_code, investor_name, business_size FROM stg.stg_users WHERE tonnage > 103\")\n", "labels": {"reads": [{"table": "stg.stg_users", "columns": ["tonnage", "marketing_region_code", "investor_name", "business_size"]}], "writes": [{"table": "org_donation", "columns": ["tonnage", "marketing_region_code", "investor_name", "business_size"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO zipcodes SELECT date_formed, received_date, pd_id FROM housing_investments WHERE date_formed > 113\"\n", "labels": {"reads": [{"table": "housing_investments", "columns": ["date_formed", "received_date", "pd_id"]}], "writes": [{"table": "zipcodes", "columns": ["date_formed", "received_date", "pd_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO dwd.products_hourly SELECT energy_efficiency_rating, complaintid FROM safe_dataset WHERE energy_efficiency_rating > 78\"\n", "labels": {"reads": [{"table": "safe_dataset", "columns": ["energy_efficiency_rating", "complaintid"]}], "writes": [{"table": "dwd.products_hourly", "columns": ["energy_efficiency_rating", "complaintid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 404;\nSQL\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_full", "columns": ["vaccinations", "park"]}, {"table": "ods.ods_payments_full", "columns": ["class_senator_vote", "genderid", "units_owned"]}], "writes": [{"table": "trainmaintenance", "columns": ["class_senator_vote", "genderid", "units_owned"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ods_risk_score_delta (engagement, booking_start_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ods_risk_score_delta", "columns": ["engagement", "booking_start_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO trends_2022 SELECT a.invested, b.vulnerability_name FROM ocean a JOIN visualartprograms b ON a.is_operational = b.is_operational\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ocean", "columns": null}, {"table": "visualartprograms", "columns": null}], "writes": [{"table": "trends_2022", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"co_ownership_program\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "co_ownership_program", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation\").toPandas()\ndf[[\"number_thousands\", \"faculty_id\"]].to_sql(\"pitstops\", engine, index=False)\n", "labels": {"reads": [{"table": "transportation", "columns": null}], "writes": [{"table": "pitstops", "columns": ["number_thousands", "faculty_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"streams\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "streams", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT incident_date, fare_date FROM smartcities\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"safety_violations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "smartcities", "columns": ["incident_date", "fare_date"]}], "writes": [{"table": "safety_violations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO league SELECT * FROM legacy\ncur.execute(\"SELECT community_members, stories FROM loan LIMIT 173\")\n", "labels": {"reads": [{"table": "loan", "columns": ["community_members", "stories"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table spacecrafts --columns state_name,trainingname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "spacecrafts", "columns": ["state_name", "trainingname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fleet_management\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"election\")\n", "labels": {"reads": [{"table": "fleet_management", "columns": null}], "writes": [{"table": "election", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"climate_communication\");\ndf.write().mode(\"overwrite\").saveAsTable(\"haircare_cruelty\");\n", "labels": {"reads": [{"table": "climate_communication", "columns": null}], "writes": [{"table": "haircare_cruelty", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO nz_tourism SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table recycling_stats --columns author,ai_model --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "recycling_stats", "columns": ["author", "ai_model"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO mart.mart_users_di SELECT measurement, medical_condition, file_size FROM canals WHERE measurement > 174\")\n", "labels": {"reads": [{"table": "canals", "columns": ["measurement", "medical_condition", "file_size"]}], "writes": [{"table": "mart.mart_users_di", "columns": ["measurement", "medical_condition", "file_size"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table labour_productivity --target-dir /tmp/land\n", "labels": {"reads": [{"table": "labour_productivity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT funding, unitsperweek FROM dwd.dwd_campaigns_df LIMIT 422\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": ["funding", "unitsperweek"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table strainlabresults --columns vehicletype,built_year --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "strainlabresults", "columns": ["vehicletype", "built_year"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO inclusive_housing SELECT nationality, size, lettergrade, airport_name FROM supply_chain WHERE nationality > 480\"\n", "labels": {"reads": [{"table": "supply_chain", "columns": ["nationality", "size", "lettergrade", "airport_name"]}], "writes": [{"table": "inclusive_housing", "columns": ["nationality", "size", "lettergrade", "airport_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"prereq\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"medical_facilities_nyc\")\n", "labels": {"reads": [{"table": "prereq", "columns": null}], "writes": [{"table": "medical_facilities_nyc", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO suppliersfairlabor SELECT time_id, policy FROM professional_development WHERE time_id > 253\")\n", "labels": {"reads": [{"table": "professional_development", "columns": ["time_id", "policy"]}], "writes": [{"table": "suppliersfairlabor", "columns": ["time_id", "policy"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO urban_farms SELECT * FROM legacy\ncur.execute(\"SELECT violationid, investor FROM wedding LIMIT 114\")\n", "labels": {"reads": [{"table": "wedding", "columns": ["violationid", "investor"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO militarydrones SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"fare_collection\")\nsrc.write.insertInto(\"drugs\", overwrite=True)\n", "labels": {"reads": [{"table": "fare_collection", "columns": null}], "writes": [{"table": "drugs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO dependent SELECT a.regionid, b.length FROM wastewater_treatment a JOIN jupiter_spacecraft b ON a.type = b.type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "wastewater_treatment", "columns": null}, {"table": "jupiter_spacecraft", "columns": null}], "writes": [{"table": "dependent", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO veteran_stats SELECT a.monthlyactiveusers, b.shipment_date FROM movie_ratings a JOIN community_policing b ON a.founder_group = b.founder_group\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "movie_ratings", "columns": null}, {"table": "community_policing", "columns": null}], "writes": [{"table": "veteran_stats", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO donorgender SELECT a.warehouse_state, b.partid FROM intelligence_personnel a JOIN diplomacy_events b ON a.facility_code = b.facility_code\"\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": null}, {"table": "diplomacy_events", "columns": null}], "writes": [{"table": "donorgender", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"clinic_2022\")\nsrc.write.insertInto(\"humanitarian_aid\", overwrite=True)\n", "labels": {"reads": [{"table": "clinic_2022", "columns": null}], "writes": [{"table": "humanitarian_aid", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 225;\nEOF\n", "labels": {"reads": [{"table": "seamounts", "columns": ["trend", "research_id", "document_id"]}], "writes": [{"table": "assignedto", "columns": ["trend", "research_id", "document_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO tv_shows_genre SELECT * FROM legacy\ncur.execute(\"SELECT bandmate, visitdate FROM elements_price LIMIT 352\")\n", "labels": {"reads": [{"table": "elements_price", "columns": ["bandmate", "visitdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"event_attendance\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "event_attendance", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"gamereviews\")\nsrc.write.insertInto(\"support_groups\", overwrite=True)\n", "labels": {"reads": [{"table": "gamereviews", "columns": null}], "writes": [{"table": "support_groups", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO ods.ods_campaigns_hourly SELECT union_name, ai_adoption, lawyer_name FROM monitoring_zones WHERE union_name > 225\");\n", "labels": {"reads": [{"table": "monitoring_zones", "columns": ["union_name", "ai_adoption", "lawyer_name"]}], "writes": [{"table": "ods.ods_campaigns_hourly", "columns": ["union_name", "ai_adoption", "lawyer_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO asset_parts SELECT * FROM legacy\ncur.execute(\"SELECT time_of_day, lipstick_id FROM trade_history LIMIT 394\")\n", "labels": {"reads": [{"table": "trade_history", "columns": ["time_of_day", "lipstick_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tourismproviders\", conn)\ndf.to_sql(\"wildlife\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tourismproviders", "columns": null}], "writes": [{"table": "wildlife", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"wrestler\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "wrestler", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"mart_shipments_full\")\nsave_to_sink(df, \"customer\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": null}], "writes": [{"table": "customer", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO bi_products SELECT founder, founder_identifies_as_lgbtq FROM org_climate_finance WHERE founder > 272\");\n", "labels": {"reads": [{"table": "org_climate_finance", "columns": ["founder", "founder_identifies_as_lgbtq"]}], "writes": [{"table": "bi_products", "columns": ["founder", "founder_identifies_as_lgbtq"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO participates_in SELECT student_id, unit_of_measure, campaign, incident_type_description FROM dancefunding WHERE student_id > 242\")\n", "labels": {"reads": [{"table": "dancefunding", "columns": ["student_id", "unit_of_measure", "campaign", "incident_type_description"]}], "writes": [{"table": "participates_in", "columns": ["student_id", "unit_of_measure", "campaign", "incident_type_description"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"aircraft\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "aircraft", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"shared_rides_tokyo\").where(\"dt = current_date()\").writeTo(\"waterconservationinitiatives\").append()\n", "labels": {"reads": [{"table": "shared_rides_tokyo", "columns": null}], "writes": [{"table": "waterconservationinitiatives", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.headquarter > 468).all()\n# src table: rural_clinics\nengine.execute(\"INSERT INTO open_pedagogy_courses SELECT * FROM rural_clinics\")\n", "labels": {"reads": [{"table": "rural_clinics", "columns": null}], "writes": [{"table": "open_pedagogy_courses", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"eventparticipation\").toPandas()\ndf[[\"application_date\", \"hub_id\"]].to_sql(\"station_crime_rates\", engine, index=False)\n", "labels": {"reads": [{"table": "eventparticipation", "columns": null}], "writes": [{"table": "station_crime_rates", "columns": ["application_date", "hub_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT portfolio_id, provider_id FROM submission LIMIT 2\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO textileworkers SELECT acidity, opname FROM address WHERE acidity > 186\")\n", "labels": {"reads": [{"table": "submission", "columns": ["portfolio_id", "provider_id"]}, {"table": "address", "columns": ["acidity", "opname"]}], "writes": [{"table": "textileworkers", "columns": ["acidity", "opname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"virtual_tours\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ref_incident_type\");\n", "labels": {"reads": [{"table": "virtual_tours", "columns": null}], "writes": [{"table": "ref_incident_type", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd.events_daily --columns days,ad_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd.events_daily", "columns": ["days", "ad_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"waterconservationbudget\").where(\"dt = current_date()\").writeTo(\"perpetrator\").append()\n", "labels": {"reads": [{"table": "waterconservationbudget", "columns": null}], "writes": [{"table": "perpetrator", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO permit SELECT lastclaimdate, risk_score, itemid, violation_count FROM inspections WHERE lastclaimdate > 64\")\n", "labels": {"reads": [{"table": "inspections", "columns": ["lastclaimdate", "risk_score", "itemid", "violation_count"]}], "writes": [{"table": "permit", "columns": ["lastclaimdate", "risk_score", "itemid", "violation_count"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO collectivebargaining SELECT individual_first_name, incident_region, booked_amount, tour_type FROM shelters WHERE individual_first_name > 424\"\n", "labels": {"reads": [{"table": "shelters", "columns": ["individual_first_name", "incident_region", "booked_amount", "tour_type"]}], "writes": [{"table": "collectivebargaining", "columns": ["individual_first_name", "incident_region", "booked_amount", "tour_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"bi_campaigns_delta\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"tv_shows_genre\")\n", "labels": {"reads": [{"table": "bi_campaigns_delta", "columns": null}], "writes": [{"table": "tv_shows_genre", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"traffic\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"authors\")\n", "labels": {"reads": [{"table": "traffic", "columns": null}], "writes": [{"table": "authors", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO roller_coaster SELECT * FROM legacy\ncur.execute(\"SELECT acc_regular_season, organized_by FROM sustainable_fabrics LIMIT 294\")\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": ["acc_regular_season", "organized_by"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO organic_cosmetics SELECT airport, continent FROM tv_shows_genre WHERE airport > 207\"\n", "labels": {"reads": [{"table": "tv_shows_genre", "columns": ["airport", "continent"]}], "writes": [{"table": "organic_cosmetics", "columns": ["airport", "continent"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO dwd.dwd_products SELECT color_code, functional_area_code, name_full FROM salesdata WHERE color_code > 419\"\n", "labels": {"reads": [{"table": "salesdata", "columns": ["color_code", "functional_area_code", "name_full"]}], "writes": [{"table": "dwd.dwd_products", "columns": ["color_code", "functional_area_code", "name_full"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO manager SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO medical_facilities_nyc SELECT delivery_date, county_name, temporary_acting FROM weather WHERE delivery_date > 346\"\n", "labels": {"reads": [{"table": "weather", "columns": ["delivery_date", "county_name", "temporary_acting"]}], "writes": [{"table": "medical_facilities_nyc", "columns": ["delivery_date", "county_name", "temporary_acting"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO community_health_workers (coupon_amount, policy_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "community_health_workers", "columns": ["coupon_amount", "policy_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO unesco_intangible_heritage SELECT a.minister, b.mean_temperature_f FROM coach a JOIN ods.ods_exposure_delta b ON a.community_size = b.community_size\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "coach", "columns": null}, {"table": "ods.ods_exposure_delta", "columns": null}], "writes": [{"table": "unesco_intangible_heritage", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mars_rovers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"carbon_offset_initiatives\")\n", "labels": {"reads": [{"table": "mars_rovers", "columns": null}], "writes": [{"table": "carbon_offset_initiatives", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT pricepergram, delivery_time FROM measurement\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"mental_health_clinics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "measurement", "columns": ["pricepergram", "delivery_time"]}], "writes": [{"table": "mental_health_clinics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT servicename, personnelid FROM stg.stg_events_hourly\", engine)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"innovation_projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stg.stg_events_hourly", "columns": ["servicename", "personnelid"]}], "writes": [{"table": "innovation_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO smartcityprojects SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM paintings\", conn)\ndf.to_sql(\"ads.payments_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "paintings", "columns": null}], "writes": [{"table": "ads.payments_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO defensespending SELECT cultural_competency_score, meter_100, nurse, policy_number FROM rd_expenditure WHERE cultural_competency_score > 261\");\n", "labels": {"reads": [{"table": "rd_expenditure", "columns": ["cultural_competency_score", "meter_100", "nurse", "policy_number"]}], "writes": [{"table": "defensespending", "columns": ["cultural_competency_score", "meter_100", "nurse", "policy_number"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"teaches\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "teaches", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_communication\"\n", "labels": {"reads": [{"table": "climate_communication", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"economic_diversification_projects\")\nsrc.write.insertInto(\"virtual_tourism\", overwrite=True)\n", "labels": {"reads": [{"table": "economic_diversification_projects", "columns": null}], "writes": [{"table": "virtual_tourism", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 328;\nEOF\n", "labels": {"reads": [{"table": "autoshows", "columns": ["chip_model", "years_played"]}], "writes": [{"table": "appointment", "columns": ["chip_model", "years_played"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO redundant_billing_data SELECT assessment_date, acc_bal, mine_name, financially_capable FROM investmentsesg WHERE assessment_date > 378\"\n", "labels": {"reads": [{"table": "investmentsesg", "columns": ["assessment_date", "acc_bal", "mine_name", "financially_capable"]}], "writes": [{"table": "redundant_billing_data", "columns": ["assessment_date", "acc_bal", "mine_name", "financially_capable"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 123;\nEOF\n", "labels": {"reads": [{"table": "enrolled_in", "columns": ["production_budget", "classid"]}], "writes": [{"table": "heritagesites", "columns": ["production_budget", "classid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO defense_contracts SELECT developer_id, class_section, game_genre FROM tickets WHERE developer_id > 26\"], check=True)\n", "labels": {"reads": [{"table": "tickets", "columns": ["developer_id", "class_section", "game_genre"]}], "writes": [{"table": "defense_contracts", "columns": ["developer_id", "class_section", "game_genre"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO marine_mammals SELECT hireid, assists FROM team_revenue WHERE hireid > 247\");\n", "labels": {"reads": [{"table": "team_revenue", "columns": ["hireid", "assists"]}], "writes": [{"table": "marine_mammals", "columns": ["hireid", "assists"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO files SELECT decoration_theme, meter_200, end_time, dataset FROM benefits_overpayments WHERE decoration_theme > 500\"\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": ["decoration_theme", "meter_200", "end_time", "dataset"]}], "writes": [{"table": "files", "columns": ["decoration_theme", "meter_200", "end_time", "dataset"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO caribbeansea SELECT round_type, source_u_id, cinema_id FROM regulatory_frameworks WHERE round_type > 340\"\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": ["round_type", "source_u_id", "cinema_id"]}], "writes": [{"table": "caribbeansea", "columns": ["round_type", "source_u_id", "cinema_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dw_orders_df SELECT fish_population, sustainability_certified, event_location, start_therapy FROM agricultural_projects WHERE fish_population > 303\"\n", "labels": {"reads": [{"table": "agricultural_projects", "columns": ["fish_population", "sustainability_certified", "event_location", "start_therapy"]}], "writes": [{"table": "dw_orders_df", "columns": ["fish_population", "sustainability_certified", "event_location", "start_therapy"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"maintenance_requests\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"daily_oil_production\")\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": null}], "writes": [{"table": "daily_oil_production", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 87;\nSQL\n", "labels": {"reads": [{"table": "stg.users", "columns": ["meal_name", "volunteerdate"]}, {"table": "rural_clinics", "columns": ["range", "safety_score", "chromosome"]}], "writes": [{"table": "tours", "columns": ["range", "safety_score", "chromosome"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"efforts\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "efforts", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO authenticationlogs SELECT a.ranking, b.user_category FROM financial_capability a JOIN socialimpactinvestments b ON a.date = b.date\"\n", "labels": {"reads": [{"table": "financial_capability", "columns": null}, {"table": "socialimpactinvestments", "columns": null}], "writes": [{"table": "authenticationlogs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mailshot_campaigns --columns energytype,mhw_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mailshot_campaigns", "columns": ["energytype", "mhw_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ods.inventory_df\", conn)\ndf.to_sql(\"bridgeconstruction\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ods.inventory_df", "columns": null}], "writes": [{"table": "bridgeconstruction", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM carbon_offset_initiatives\", conn)\ndf.to_sql(\"item_prices\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "carbon_offset_initiatives", "columns": null}], "writes": [{"table": "item_prices", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO makeup_sales SELECT total_amount, strain_type, vrgameid FROM public.crime_types WHERE total_amount > 295\");\n", "labels": {"reads": [{"table": "public.crime_types", "columns": ["total_amount", "strain_type", "vrgameid"]}], "writes": [{"table": "makeup_sales", "columns": ["total_amount", "strain_type", "vrgameid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO ngo_funding SELECT a.production_quantity, b.movie FROM exam_results a JOIN materials_usage b ON a.fair_trade = b.fair_trade\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "exam_results", "columns": null}, {"table": "materials_usage", "columns": null}], "writes": [{"table": "ngo_funding", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO recreation_centers SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table biosensors.projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO courtcases SELECT birthday, porphyria, survey_id FROM community_development_projects WHERE birthday > 180\")\n", "labels": {"reads": [{"table": "community_development_projects", "columns": ["birthday", "porphyria", "survey_id"]}], "writes": [{"table": "courtcases", "columns": ["birthday", "porphyria", "survey_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO humanitarianassistanceoperations SELECT crane_id, sessiondate, lieutenant_governor, city_traffic_speed FROM instructor WHERE crane_id > 213\"], check=True)\n", "labels": {"reads": [{"table": "instructor", "columns": ["crane_id", "sessiondate", "lieutenant_governor", "city_traffic_speed"]}], "writes": [{"table": "humanitarianassistanceoperations", "columns": ["crane_id", "sessiondate", "lieutenant_governor", "city_traffic_speed"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"skincareproducts\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "skincareproducts", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO submarine_canyons SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO trip SELECT semester, volunteer_quarter FROM cosmetics WHERE semester > 360\")\n", "labels": {"reads": [{"table": "cosmetics", "columns": ["semester", "volunteer_quarter"]}], "writes": [{"table": "trip", "columns": ["semester", "volunteer_quarter"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wastewater_plants SELECT * FROM legacy\ncur.execute(\"SELECT is_commercial, reporter_id FROM charging_stations LIMIT 318\")\n", "labels": {"reads": [{"table": "charging_stations", "columns": ["is_commercial", "reporter_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO medals SELECT deaths, text FROM beverages WHERE deaths > 26\"\n", "labels": {"reads": [{"table": "beverages", "columns": ["deaths", "text"]}], "writes": [{"table": "medals", "columns": ["deaths", "text"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO budget SELECT date_assigned_from, green_building_certified FROM postseason WHERE date_assigned_from > 161\");\n", "labels": {"reads": [{"table": "postseason", "columns": ["date_assigned_from", "green_building_certified"]}], "writes": [{"table": "budget", "columns": ["date_assigned_from", "green_building_certified"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO fieldd_info SELECT opponent_id, recruitername FROM volunteer_hours WHERE opponent_id > 88\")\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": ["opponent_id", "recruitername"]}], "writes": [{"table": "fieldd_info", "columns": ["opponent_id", "recruitername"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"co2_emissions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"organic_products\")\n", "labels": {"reads": [{"table": "co2_emissions", "columns": null}], "writes": [{"table": "organic_products", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO financial_capability_id SELECT teacher_id, patentexpirationdate, organisation_details FROM city.community_policing WHERE teacher_id > 69\")\n", "labels": {"reads": [{"table": "city.community_policing", "columns": ["teacher_id", "patentexpirationdate", "organisation_details"]}], "writes": [{"table": "financial_capability_id", "columns": ["teacher_id", "patentexpirationdate", "organisation_details"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO open_pedagogy SELECT time_stamp, transaction_amount, frequency FROM az_drought_impact WHERE time_stamp > 126\"\n", "labels": {"reads": [{"table": "az_drought_impact", "columns": ["time_stamp", "transaction_amount", "frequency"]}], "writes": [{"table": "open_pedagogy", "columns": ["time_stamp", "transaction_amount", "frequency"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dws_events_di SELECT athleteid, warehouse_state FROM news_report WHERE athleteid > 152\"\n", "labels": {"reads": [{"table": "news_report", "columns": ["athleteid", "warehouse_state"]}], "writes": [{"table": "dws_events_di", "columns": ["athleteid", "warehouse_state"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dw.users_hourly (form_name, state_province_county) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dw.users_hourly", "columns": ["form_name", "state_province_county"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO business_rates SELECT eventid, meal_id, thefttype, date_of_publication FROM researchgrants WHERE eventid > 111\"\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["eventid", "meal_id", "thefttype", "date_of_publication"]}], "writes": [{"table": "business_rates", "columns": ["eventid", "meal_id", "thefttype", "date_of_publication"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model equipmentsales depends on military_expenditure\ndbt build --select equipmentsales --vars '{\"src\":\"military_expenditure\"}'\n", "labels": {"reads": [{"table": "military_expenditure", "columns": null}], "writes": [{"table": "equipmentsales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO production_costs SELECT accessibility, building_full_name, classroom, platformid FROM sustainability_metrics WHERE accessibility > 424\");\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": ["accessibility", "building_full_name", "classroom", "platformid"]}], "writes": [{"table": "production_costs", "columns": ["accessibility", "building_full_name", "classroom", "platformid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ingredientsvegancrueltyfree SELECT * FROM legacy\ncur.execute(\"SELECT profession_count, shipment_id FROM storage_projects LIMIT 447\")\n", "labels": {"reads": [{"table": "storage_projects", "columns": ["profession_count", "shipment_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT process_id, shipmentid FROM chemicalbatches LIMIT 244\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "chemicalbatches", "columns": ["process_id", "shipmentid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.fish_population > 330).all()\n# src table: plankton\nengine.execute(\"INSERT INTO mart.mart_campaigns_daily SELECT * FROM plankton\")\n", "labels": {"reads": [{"table": "plankton", "columns": null}], "writes": [{"table": "mart.mart_campaigns_daily", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM contracts\"\n", "labels": {"reads": [{"table": "contracts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO accommodations SELECT vehicle_type, market, advisor, art_id FROM culturalpractices WHERE vehicle_type > 201\"], check=True)\n", "labels": {"reads": [{"table": "culturalpractices", "columns": ["vehicle_type", "market", "advisor", "art_id"]}], "writes": [{"table": "accommodations", "columns": ["vehicle_type", "market", "advisor", "art_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO multimodalhubs SELECT hours_played, authors, garment_type FROM communitycourtcases WHERE hours_played > 394\"\n", "labels": {"reads": [{"table": "communitycourtcases", "columns": ["hours_played", "authors", "garment_type"]}], "writes": [{"table": "multimodalhubs", "columns": ["hours_played", "authors", "garment_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO patient_satisfaction SELECT a.researcher_id, b.labordate FROM uniteddefense.equipmentsales a JOIN dw_risk_score_daily b ON a.source_u_id = b.source_u_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "uniteddefense.equipmentsales", "columns": null}, {"table": "dw_risk_score_daily", "columns": null}], "writes": [{"table": "patient_satisfaction", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO organization_contact_individuals SELECT amount_paid, reported_date FROM temperature WHERE amount_paid > 100\"\n", "labels": {"reads": [{"table": "temperature", "columns": ["amount_paid", "reported_date"]}], "writes": [{"table": "organization_contact_individuals", "columns": ["amount_paid", "reported_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT production_mwh, star_rating_description FROM trips LIMIT 258\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "trips", "columns": ["production_mwh", "star_rating_description"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO zipcodes SELECT a.co2_reduction, b.education_id FROM green_building_projects a JOIN apartment_facilities b ON a.seat_section = b.seat_section\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "green_building_projects", "columns": null}, {"table": "apartment_facilities", "columns": null}], "writes": [{"table": "zipcodes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO asia_events SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO domesticconferences SELECT ship_id, operationdate FROM gradeconversion WHERE ship_id > 451\"\n", "labels": {"reads": [{"table": "gradeconversion", "columns": ["ship_id", "operationdate"]}], "writes": [{"table": "domesticconferences", "columns": ["ship_id", "operationdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT worker_count, athlete FROM company\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"restaurants\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "company", "columns": ["worker_count", "athlete"]}], "writes": [{"table": "restaurants", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO chemicals SELECT * FROM legacy\nspark.sql(\"INSERT INTO policyanalysis SELECT participant_details, fish_id, lettergrade, billingcountry FROM drought_data WHERE participant_details > 230\")\n", "labels": {"reads": [{"table": "drought_data", "columns": ["participant_details", "fish_id", "lettergrade", "billingcountry"]}], "writes": [{"table": "policyanalysis", "columns": ["participant_details", "fish_id", "lettergrade", "billingcountry"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart.risk_score_df SELECT circular_supply_chain, lipstick_id, affirmative, rid FROM has_amenity WHERE circular_supply_chain > 142\"\n", "labels": {"reads": [{"table": "has_amenity", "columns": ["circular_supply_chain", "lipstick_id", "affirmative", "rid"]}], "writes": [{"table": "mart.risk_score_df", "columns": ["circular_supply_chain", "lipstick_id", "affirmative", "rid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"doctors\").where(\"dt = current_date()\").writeTo(\"school\").append()\n", "labels": {"reads": [{"table": "doctors", "columns": null}], "writes": [{"table": "school", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"machine_emissions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"co2emissions\")\n", "labels": {"reads": [{"table": "machine_emissions", "columns": null}], "writes": [{"table": "co2emissions", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO oceania_countries SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vessels SELECT factory_id, mean_temperature_f, species_id FROM ads_vendors_hourly WHERE factory_id > 459\"\n", "labels": {"reads": [{"table": "ads_vendors_hourly", "columns": ["factory_id", "mean_temperature_f", "species_id"]}], "writes": [{"table": "vessels", "columns": ["factory_id", "mean_temperature_f", "species_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ref_detention_type SELECT projecttype, sessiondate, pass_fail FROM caseattorneys WHERE projecttype > 133\")\n", "labels": {"reads": [{"table": "caseattorneys", "columns": ["projecttype", "sessiondate", "pass_fail"]}], "writes": [{"table": "ref_detention_type", "columns": ["projecttype", "sessiondate", "pass_fail"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO paris_train (worker_id, quantitysold) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "paris_train", "columns": ["worker_id", "quantitysold"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT membername, author_or_editor FROM cars LIMIT 93\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "cars", "columns": ["membername", "author_or_editor"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"pilot_record\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"hospitallocations\")\n", "labels": {"reads": [{"table": "pilot_record", "columns": null}], "writes": [{"table": "hospitallocations", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO bike_station_info SELECT start_speed, month, recycler_id FROM all_programs WHERE start_speed > 339\")\n", "labels": {"reads": [{"table": "all_programs", "columns": ["start_speed", "month", "recycler_id"]}], "writes": [{"table": "bike_station_info", "columns": ["start_speed", "month", "recycler_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"bi_refunds_daily\")\ndump_to_output(df, \"city_waste_generation\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": null}], "writes": [{"table": "city_waste_generation", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd.coupon_use_full SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO carbon_prices SELECT * FROM legacy\ncur.execute(\"SELECT vehicle_flight_number, make FROM oil_production LIMIT 195\")\n", "labels": {"reads": [{"table": "oil_production", "columns": ["vehicle_flight_number", "make"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"screen_mode\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"prison\")\n", "labels": {"reads": [{"table": "screen_mode", "columns": null}], "writes": [{"table": "prison", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO soil_moisture SELECT frameworkcountry, program_category FROM dws.dws_refunds_daily WHERE frameworkcountry > 95\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_daily", "columns": ["frameworkcountry", "program_category"]}], "writes": [{"table": "soil_moisture", "columns": ["frameworkcountry", "program_category"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_source(ctx, \"tree_habitat_associations\")\nsave_to_sink(df, \"dwd.sessions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tree_habitat_associations", "columns": null}], "writes": [{"table": "dwd.sessions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"extraction_methods\").where(\"dt = current_date()\").writeTo(\"customer\").append()\n", "labels": {"reads": [{"table": "extraction_methods", "columns": null}], "writes": [{"table": "customer", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_address_history\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "customer_address_history", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM artpieces\"\n", "labels": {"reads": [{"table": "artpieces", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO hall_of_fame SELECT sea, members, healthcareid FROM yttrium_production WHERE sea > 279\");\n", "labels": {"reads": [{"table": "yttrium_production", "columns": ["sea", "members", "healthcareid"]}], "writes": [{"table": "hall_of_fame", "columns": ["sea", "members", "healthcareid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO farmer_details SELECT mentalhealthscore, plant_name, training_id FROM concert WHERE mentalhealthscore > 231\"\n", "labels": {"reads": [{"table": "concert", "columns": ["mentalhealthscore", "plant_name", "training_id"]}], "writes": [{"table": "farmer_details", "columns": ["mentalhealthscore", "plant_name", "training_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"workercontactinfo\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"mart.mart_vendors\")\n", "labels": {"reads": [{"table": "workercontactinfo", "columns": null}], "writes": [{"table": "mart.mart_vendors", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.animal > 114).all()\n# src table: cb_agreements\nengine.execute(\"INSERT INTO party_forms SELECT * FROM cb_agreements\")\n", "labels": {"reads": [{"table": "cb_agreements", "columns": null}], "writes": [{"table": "party_forms", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO flu_shots SELECT calendar_date, socialimpactscore FROM mart.mart_coupon_use_delta WHERE calendar_date > 313\"\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": ["calendar_date", "socialimpactscore"]}], "writes": [{"table": "flu_shots", "columns": ["calendar_date", "socialimpactscore"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table total_capacity --target-dir /tmp/land\n", "labels": {"reads": [{"table": "total_capacity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO seeds SELECT airline, num_of_factories FROM postseason WHERE airline > 485\");\n", "labels": {"reads": [{"table": "postseason", "columns": ["airline", "num_of_factories"]}], "writes": [{"table": "seeds", "columns": ["airline", "num_of_factories"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO subway_stations_seoul SELECT unitsperweek, countid FROM mexico_regions WHERE unitsperweek > 336\"\n", "labels": {"reads": [{"table": "mexico_regions", "columns": ["unitsperweek", "countid"]}], "writes": [{"table": "subway_stations_seoul", "columns": ["unitsperweek", "countid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT rig_id, tournament_id FROM ocean_species LIMIT 462\")\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO communityengagements SELECT transit_passengers, sale_id, bdate FROM jupiter_missions WHERE transit_passengers > 284\")\n", "labels": {"reads": [{"table": "ocean_species", "columns": ["rig_id", "tournament_id"]}, {"table": "jupiter_missions", "columns": ["transit_passengers", "sale_id", "bdate"]}], "writes": [{"table": "communityengagements", "columns": ["transit_passengers", "sale_id", "bdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO carbon_offset_programs SELECT cloud_cover, date_opened, streams FROM bi.bi_sessions_daily WHERE cloud_cover > 237\")\n", "labels": {"reads": [{"table": "bi.bi_sessions_daily", "columns": ["cloud_cover", "date_opened", "streams"]}], "writes": [{"table": "carbon_offset_programs", "columns": ["cloud_cover", "date_opened", "streams"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM electric_vehicles\"\n", "labels": {"reads": [{"table": "electric_vehicles", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"drug_approval\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"waste_data\")\n", "labels": {"reads": [{"table": "drug_approval", "columns": null}], "writes": [{"table": "waste_data", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO fleet_management SELECT node_id, testtypeid, case_status, author_community FROM staff_department_assignments WHERE node_id > 157\"\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["node_id", "testtypeid", "case_status", "author_community"]}], "writes": [{"table": "fleet_management", "columns": ["node_id", "testtypeid", "case_status", "author_community"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT restypedescription, department FROM missions LIMIT 58\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO locations SELECT measurement_date, license_type, event_count, sale_volume FROM mart.mart_users_delta WHERE measurement_date > 161\")\n", "labels": {"reads": [{"table": "missions", "columns": ["restypedescription", "department"]}, {"table": "mart.mart_users_delta", "columns": ["measurement_date", "license_type", "event_count", "sale_volume"]}], "writes": [{"table": "locations", "columns": ["measurement_date", "license_type", "event_count", "sale_volume"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"student\").where(\"dt = current_date()\").writeTo(\"deep_sea_expeditions\").append()\n", "labels": {"reads": [{"table": "student", "columns": null}], "writes": [{"table": "deep_sea_expeditions", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 408;\nSQL\n", "labels": {"reads": [{"table": "studentaccommodations", "columns": ["courtname", "volume"]}, {"table": "dws.dws_inventory_hourly", "columns": ["fda_approved", "ingredient_id", "treatment"]}], "writes": [{"table": "mexico_regions", "columns": ["fda_approved", "ingredient_id", "treatment"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT initiative_type, artist_nationality FROM fertilizer LIMIT 419\")\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO artist_data SELECT visit_id, batting_average, date_left_staff FROM workout_sessions WHERE visit_id > 84\")\n", "labels": {"reads": [{"table": "fertilizer", "columns": ["initiative_type", "artist_nationality"]}, {"table": "workout_sessions", "columns": ["visit_id", "batting_average", "date_left_staff"]}], "writes": [{"table": "artist_data", "columns": ["visit_id", "batting_average", "date_left_staff"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ethicalaibudget\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"open_data_initiatives\")\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": [{"table": "open_data_initiatives", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"nasa_mars_program\").where(\"dt = current_date()\").writeTo(\"navalvessels\").append()\n", "labels": {"reads": [{"table": "nasa_mars_program", "columns": null}], "writes": [{"table": "navalvessels", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO thefttypes SELECT num_pallets, business_id, policy_number, hub_id FROM biomes WHERE num_pallets > 211\");\n", "labels": {"reads": [{"table": "biomes", "columns": ["num_pallets", "business_id", "policy_number", "hub_id"]}], "writes": [{"table": "thefttypes", "columns": ["num_pallets", "business_id", "policy_number", "hub_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 89;\nSQL\n", "labels": {"reads": [{"table": "customer", "columns": ["characteristic_name", "bdate"]}, {"table": "mart.mart_users_di", "columns": ["county_id", "volunteer_id", "railway_id", "goal_id"]}], "writes": [{"table": "rebounds", "columns": ["county_id", "volunteer_id", "railway_id", "goal_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewableprojects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "renewableprojects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO factory_connections SELECT ethical_certifications, org_id FROM dws.dws_users_hourly WHERE ethical_certifications > 288\");\n", "labels": {"reads": [{"table": "dws.dws_users_hourly", "columns": ["ethical_certifications", "org_id"]}], "writes": [{"table": "factory_connections", "columns": ["ethical_certifications", "org_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"recycledmaterialsgarments\").where(\"dt = current_date()\").writeTo(\"experience\").append()\n", "labels": {"reads": [{"table": "recycledmaterialsgarments", "columns": null}], "writes": [{"table": "experience", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT daily_sales, part_id FROM buildings\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"renewableenergy\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "buildings", "columns": ["daily_sales", "part_id"]}], "writes": [{"table": "renewableenergy", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT partitionid, number_of_platforms FROM fairtradecertification LIMIT 46\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "fairtradecertification", "columns": ["partitionid", "number_of_platforms"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO community_health_centers SELECT gametype, number_city_affected, projectid, emp_jobcode FROM carbon_footprint WHERE gametype > 275\"\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": ["gametype", "number_city_affected", "projectid", "emp_jobcode"]}], "writes": [{"table": "community_health_centers", "columns": ["gametype", "number_city_affected", "projectid", "emp_jobcode"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO behavior_incident SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO parity_violations SELECT scientific_name, farm_id, venue FROM invoice WHERE scientific_name > 117\"\n", "labels": {"reads": [{"table": "invoice", "columns": ["scientific_name", "farm_id", "venue"]}], "writes": [{"table": "parity_violations", "columns": ["scientific_name", "farm_id", "venue"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT annual_carbon_offsets, labor_id FROM menuitems LIMIT 83\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "menuitems", "columns": ["annual_carbon_offsets", "labor_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO co2_sequestration (carbon_footprint, fund_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "co2_sequestration", "columns": ["carbon_footprint", "fund_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"broadband_revenue\")\nsrc.write.insertInto(\"social_good_education\", overwrite=True)\n", "labels": {"reads": [{"table": "broadband_revenue", "columns": null}], "writes": [{"table": "social_good_education", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO waste_management_projects SELECT post_category, num_songs, max_aperture, mission_count FROM rating WHERE post_category > 124\")\n", "labels": {"reads": [{"table": "rating", "columns": ["post_category", "num_songs", "max_aperture", "mission_count"]}], "writes": [{"table": "waste_management_projects", "columns": ["post_category", "num_songs", "max_aperture", "mission_count"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 378;\nEOF\n", "labels": {"reads": [{"table": "innovation_projects", "columns": ["testdate", "area_ha", "response_time", "charging_level"]}], "writes": [{"table": "france_culture", "columns": ["testdate", "area_ha", "response_time", "charging_level"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO ads.orders SELECT component_type, lanes, therapy_sessions, acidity_level FROM news_reporting WHERE component_type > 136\")\n", "labels": {"reads": [{"table": "news_reporting", "columns": ["component_type", "lanes", "therapy_sessions", "acidity_level"]}], "writes": [{"table": "ads.orders", "columns": ["component_type", "lanes", "therapy_sessions", "acidity_level"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO efforts SELECT helpful_votes, leader_name FROM staff_department_assignments WHERE helpful_votes > 478\")\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["helpful_votes", "leader_name"]}], "writes": [{"table": "efforts", "columns": ["helpful_votes", "leader_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT max_cargo_weight, theftdate FROM workforce_training\", engine)\nimport logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"rural_feeder_roads\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "workforce_training", "columns": ["max_cargo_weight", "theftdate"]}], "writes": [{"table": "rural_feeder_roads", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 479;\nEOF\n", "labels": {"reads": [{"table": "investments_esg", "columns": ["reported_by_staff_id", "date_closed", "workforce_development", "therapy_date"]}], "writes": [{"table": "carbon_prices", "columns": ["reported_by_staff_id", "date_closed", "workforce_development", "therapy_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO market_share SELECT projectid, surname, observation_id, max_wind_speed_mph FROM budget_allocations WHERE projectid > 4\"\n", "labels": {"reads": [{"table": "budget_allocations", "columns": ["projectid", "surname", "observation_id", "max_wind_speed_mph"]}], "writes": [{"table": "market_share", "columns": ["projectid", "surname", "observation_id", "max_wind_speed_mph"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_species_observations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "marine_species_observations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT valuation, ihsaa_football_class FROM operations LIMIT 344\")\nresult = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO farmers SELECT threat, form_id, hardware_model_name, license_id FROM dw_orders_df WHERE threat > 223\")\n", "labels": {"reads": [{"table": "operations", "columns": ["valuation", "ihsaa_football_class"]}, {"table": "dw_orders_df", "columns": ["threat", "form_id", "hardware_model_name", "license_id"]}], "writes": [{"table": "farmers", "columns": ["threat", "form_id", "hardware_model_name", "license_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO pharmasales SELECT a.login_name, b.coal_reserve_remaining FROM vrplayers a JOIN mental_health_professionals_2 b ON a.schedule_date = b.schedule_date\"\n", "labels": {"reads": [{"table": "vrplayers", "columns": null}, {"table": "mental_health_professionals_2", "columns": null}], "writes": [{"table": "pharmasales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table electricvehicles --columns initiativename,claim_stage_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "electricvehicles", "columns": ["initiativename", "claim_stage_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"program\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"biomes\")\n", "labels": {"reads": [{"table": "program", "columns": null}], "writes": [{"table": "biomes", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table hotel_reviews --target-dir /tmp/land\n", "labels": {"reads": [{"table": "hotel_reviews", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"marketing_regions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "marketing_regions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM inmates\", conn)\ndf.to_sql(\"public.ev_sales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "inmates", "columns": null}], "writes": [{"table": "public.ev_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 325;\nSQL\n", "labels": {"reads": [{"table": "climateresearch", "columns": ["tonnage", "course_completion"]}, {"table": "event", "columns": ["explainability_score", "evaluated_for_fairness", "analysis_date"]}], "writes": [{"table": "networkdevices", "columns": ["explainability_score", "evaluated_for_fairness", "analysis_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO daily_transaction_volume SELECT a.eliminated_by, b.mean_visibility_miles FROM renewable_projects a JOIN founders b ON a.domestic_passengers = b.domestic_passengers\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}, {"table": "founders", "columns": null}], "writes": [{"table": "daily_transaction_volume", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"wastewatertreatment\")\nsrc.write.insertInto(\"follows\", overwrite=True)\n", "labels": {"reads": [{"table": "wastewatertreatment", "columns": null}], "writes": [{"table": "follows", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO energy_production SELECT build_year, suppliername, event_name, yield_id FROM flu_cases WHERE build_year > 190\"\n", "labels": {"reads": [{"table": "flu_cases", "columns": ["build_year", "suppliername", "event_name", "yield_id"]}], "writes": [{"table": "energy_production", "columns": ["build_year", "suppliername", "event_name", "yield_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"worker_union\")\nsrc.write.insertInto(\"biotech.startups\", overwrite=True)\n", "labels": {"reads": [{"table": "worker_union", "columns": null}], "writes": [{"table": "biotech.startups", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads.ads_exposure_di (appelation, transaction_value) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_exposure_di", "columns": ["appelation", "transaction_value"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donations_insert_2\").toPandas()\ndf[[\"countryname\", \"pid\"]].to_sql(\"teacher_development_race\", engine, index=False)\n", "labels": {"reads": [{"table": "donations_insert_2", "columns": null}], "writes": [{"table": "teacher_development_race", "columns": ["countryname", "pid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table wildlife_sanctuaries --columns registered_date,rec_engine --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "wildlife_sanctuaries", "columns": ["registered_date", "rec_engine"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO diversification_projects (subscription_start_date, eventdate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "diversification_projects", "columns": ["subscription_start_date", "eventdate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO pilot SELECT ai_id, fund_type, decision, scan_date FROM wind_turbines WHERE ai_id > 282\"], check=True)\n", "labels": {"reads": [{"table": "wind_turbines", "columns": ["ai_id", "fund_type", "decision", "scan_date"]}], "writes": [{"table": "pilot", "columns": ["ai_id", "fund_type", "decision", "scan_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO flight_safety SELECT customer_first_name, gross_worldwide, stu_lname, actor_name FROM diversity WHERE customer_first_name > 418\");\n", "labels": {"reads": [{"table": "diversity", "columns": ["customer_first_name", "gross_worldwide", "stu_lname", "actor_name"]}], "writes": [{"table": "flight_safety", "columns": ["customer_first_name", "gross_worldwide", "stu_lname", "actor_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO nursing_homes SELECT a.start_date, b.languageid FROM playerscores a JOIN certifications b ON a.alid = b.alid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "playerscores", "columns": null}, {"table": "certifications", "columns": null}], "writes": [{"table": "nursing_homes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO reviews SELECT u_id, claim_status_description, date_closed, id FROM publicchargingstations WHERE u_id > 181\"\n", "labels": {"reads": [{"table": "publicchargingstations", "columns": ["u_id", "claim_status_description", "date_closed", "id"]}], "writes": [{"table": "reviews", "columns": ["u_id", "claim_status_description", "date_closed", "id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT digital_asset, healthcareid FROM productsafety LIMIT 147\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "productsafety", "columns": ["digital_asset", "healthcareid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT away_team, problem_id FROM transportation_per_country\", engine)\nimport logging\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"biosensors.projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": ["away_team", "problem_id"]}], "writes": [{"table": "biosensors.projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_shipments_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"trafficviolations\")\n", "labels": {"reads": [{"table": "ods_shipments_df", "columns": null}], "writes": [{"table": "trafficviolations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table culturalcompetencytrainings --columns crime_type,vessel_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "culturalcompetencytrainings", "columns": ["crime_type", "vessel_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO fault_log_parts SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM daily_oil_production\", conn)\ndf.to_sql(\"paper_data\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": null}], "writes": [{"table": "paper_data", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO production_rare_earth_elements (artpiecename, last_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "production_rare_earth_elements", "columns": ["artpiecename", "last_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 237;\nEOF\n", "labels": {"reads": [{"table": "gymnast", "columns": ["recorded_by_staff_id", "player_api_id"]}], "writes": [{"table": "veteran_employment", "columns": ["recorded_by_staff_id", "player_api_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO patents SELECT stream_date, component_name, provider_name, model FROM third_party_companies WHERE stream_date > 220\"\n", "labels": {"reads": [{"table": "third_party_companies", "columns": ["stream_date", "component_name", "provider_name", "model"]}], "writes": [{"table": "patents", "columns": ["stream_date", "component_name", "provider_name", "model"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO eventdates SELECT market_value_in_billion, unitprice, attribute_value, accommodation_type FROM livestock WHERE market_value_in_billion > 380\");\n", "labels": {"reads": [{"table": "livestock", "columns": ["market_value_in_billion", "unitprice", "attribute_value", "accommodation_type"]}], "writes": [{"table": "eventdates", "columns": ["market_value_in_billion", "unitprice", "attribute_value", "accommodation_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT invoice_id, total_donation_amount FROM hall_of_fame\", engine)\nimport logging\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"investor_activities\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hall_of_fame", "columns": ["invoice_id", "total_donation_amount"]}], "writes": [{"table": "investor_activities", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO facility_production SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO recycling_rates SELECT sanctuary, flight_number FROM recyclers WHERE sanctuary > 427\")\n", "labels": {"reads": [{"table": "recyclers", "columns": ["sanctuary", "flight_number"]}], "writes": [{"table": "recycling_rates", "columns": ["sanctuary", "flight_number"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO region SELECT donor_state, home_city, port_id, fare FROM ocean_depths WHERE donor_state > 323\");\n", "labels": {"reads": [{"table": "ocean_depths", "columns": ["donor_state", "home_city", "port_id", "fare"]}], "writes": [{"table": "region", "columns": ["donor_state", "home_city", "port_id", "fare"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO campaigns_2023 SELECT participant_type_code, fan_age, emp_num FROM vocals WHERE participant_type_code > 451\"\n", "labels": {"reads": [{"table": "vocals", "columns": ["participant_type_code", "fan_age", "emp_num"]}], "writes": [{"table": "campaigns_2023", "columns": ["participant_type_code", "fan_age", "emp_num"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO vessel SELECT worker_name, shipmenttype, points_per_game FROM party_forms WHERE worker_name > 331\"\n", "labels": {"reads": [{"table": "party_forms", "columns": ["worker_name", "shipmenttype", "points_per_game"]}], "writes": [{"table": "vessel", "columns": ["worker_name", "shipmenttype", "points_per_game"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO lead_mines (type_of_thing_code, site_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "lead_mines", "columns": ["type_of_thing_code", "site_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM professional_development\", conn)\ndf.to_sql(\"stg.stg_device_log_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "professional_development", "columns": null}], "writes": [{"table": "stg.stg_device_log_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"incident\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"route\")\n", "labels": {"reads": [{"table": "incident", "columns": null}], "writes": [{"table": "route", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT mine_name, active FROM paper_data LIMIT 161\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ref_calendar SELECT satelliteid, election_cycle, committee FROM volume WHERE satelliteid > 201\")\n", "labels": {"reads": [{"table": "paper_data", "columns": ["mine_name", "active"]}, {"table": "volume", "columns": ["satelliteid", "election_cycle", "committee"]}], "writes": [{"table": "ref_calendar", "columns": ["satelliteid", "election_cycle", "committee"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT lender_name, appointment_date FROM document_functional_areas LIMIT 386\")\nimport logging\nspark.sql(\"INSERT INTO ingredient_sourcing SELECT vote_percent, customer_address_id, oct, reporter_id FROM document_sections_images WHERE vote_percent > 381\")\n", "labels": {"reads": [{"table": "document_functional_areas", "columns": ["lender_name", "appointment_date"]}, {"table": "document_sections_images", "columns": ["vote_percent", "customer_address_id", "oct", "reporter_id"]}], "writes": [{"table": "ingredient_sourcing", "columns": ["vote_percent", "customer_address_id", "oct", "reporter_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"warehouses\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "warehouses", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO communityhealthworkers SELECT directed_by, mentalhealthscore, trainingname FROM pets WHERE directed_by > 115\");\n", "labels": {"reads": [{"table": "pets", "columns": ["directed_by", "mentalhealthscore", "trainingname"]}], "writes": [{"table": "communityhealthworkers", "columns": ["directed_by", "mentalhealthscore", "trainingname"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dwd.dwd_exposure_df\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"collections\")\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_df", "columns": null}], "writes": [{"table": "collections", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM habitat\", conn)\ndf.to_sql(\"mental_health_parity\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "habitat", "columns": null}], "writes": [{"table": "mental_health_parity", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO office_locations SELECT user_login, race FROM user_ad_interactions WHERE user_login > 373\"\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": ["user_login", "race"]}], "writes": [{"table": "office_locations", "columns": ["user_login", "race"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT transaction_product, line_1 FROM video_games LIMIT 317\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "video_games", "columns": ["transaction_product", "line_1"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mentalhealthscores\").where(\"dt = current_date()\").writeTo(\"crime_incidents\").append()\n", "labels": {"reads": [{"table": "mentalhealthscores", "columns": null}], "writes": [{"table": "crime_incidents", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"agroecology_practices\")\npush_to_output(df, \"victims\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "agroecology_practices", "columns": null}], "writes": [{"table": "victims", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO hotel_revenue SELECT party_name, screening_id, medical_condition, device_id FROM intelligenceoperations WHERE party_name > 83\"\n", "labels": {"reads": [{"table": "intelligenceoperations", "columns": ["party_name", "screening_id", "medical_condition", "device_id"]}], "writes": [{"table": "hotel_revenue", "columns": ["party_name", "screening_id", "medical_condition", "device_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO exoplanet_discoveries SELECT a.recycler_id, b.account_number FROM stg.device_log_df a JOIN organic_cosmetics b ON a.hardware_colours = b.hardware_colours\"\n", "labels": {"reads": [{"table": "stg.device_log_df", "columns": null}, {"table": "organic_cosmetics", "columns": null}], "writes": [{"table": "exoplanet_discoveries", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM researchprojects\"\n", "labels": {"reads": [{"table": "researchprojects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.campaigns_di\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dws.dws_shipments_full\")\n", "labels": {"reads": [{"table": "ods.campaigns_di", "columns": null}], "writes": [{"table": "dws.dws_shipments_full", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 186;\nSQL\n", "labels": {"reads": [{"table": "product_sales", "columns": ["dept_id", "hub_id"]}, {"table": "electricvehiclestats", "columns": ["complaint_date", "complaint_status_code", "siteid"]}], "writes": [{"table": "dwd_coupon_use_hourly", "columns": ["complaint_date", "complaint_status_code", "siteid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO space_exploration SELECT * FROM legacy\nspark.sql(\"INSERT INTO campaigns_2023 SELECT storename, thing_id, courses, headquartered_city FROM humanitarian_operations WHERE storename > 238\")\n", "labels": {"reads": [{"table": "humanitarian_operations", "columns": ["storename", "thing_id", "courses", "headquartered_city"]}], "writes": [{"table": "campaigns_2023", "columns": ["storename", "thing_id", "courses", "headquartered_city"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO automation_tech SELECT ocean_name, num_songs FROM broadband_plans WHERE ocean_name > 412\")\n", "labels": {"reads": [{"table": "broadband_plans", "columns": ["ocean_name", "num_songs"]}], "writes": [{"table": "automation_tech", "columns": ["ocean_name", "num_songs"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO district (artpiecename, network) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "district", "columns": ["artpiecename", "network"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd_risk_score_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg_orders_hourly\")\n", "labels": {"reads": [{"table": "dwd_risk_score_hourly", "columns": null}], "writes": [{"table": "stg_orders_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT algorithm, dispensary_name FROM vessel_registry LIMIT 118\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO exoplanet_discoveries SELECT detention_summary, startup_id, community, last_maintenance_date FROM victims WHERE detention_summary > 148\")\n", "labels": {"reads": [{"table": "vessel_registry", "columns": ["algorithm", "dispensary_name"]}, {"table": "victims", "columns": ["detention_summary", "startup_id", "community", "last_maintenance_date"]}], "writes": [{"table": "exoplanet_discoveries", "columns": ["detention_summary", "startup_id", "community", "last_maintenance_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO imagery_archive SELECT operation_name, effort, is_sustainable, business_name FROM recovery_program WHERE operation_name > 309\"\n", "labels": {"reads": [{"table": "recovery_program", "columns": ["operation_name", "effort", "is_sustainable", "business_name"]}], "writes": [{"table": "imagery_archive", "columns": ["operation_name", "effort", "is_sustainable", "business_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"brands\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "brands", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO total_capacity SELECT number_of_sightings, monthlyactiveusers FROM artists_valuation WHERE number_of_sightings > 301\"\n", "labels": {"reads": [{"table": "artists_valuation", "columns": ["number_of_sightings", "monthlyactiveusers"]}], "writes": [{"table": "total_capacity", "columns": ["number_of_sightings", "monthlyactiveusers"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"police_emergencies\");\ndf.write().mode(\"overwrite\").saveAsTable(\"jp_schema.policy_areas\");\n", "labels": {"reads": [{"table": "police_emergencies", "columns": null}], "writes": [{"table": "jp_schema.policy_areas", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO investor_activities SELECT goals, innovation, section_id FROM marine_conservation WHERE goals > 411\"], check=True)\n", "labels": {"reads": [{"table": "marine_conservation", "columns": ["goals", "innovation", "section_id"]}], "writes": [{"table": "investor_activities", "columns": ["goals", "innovation", "section_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"cultural_events\")\nsrc.write.insertInto(\"regular_order_products\", overwrite=True)\n", "labels": {"reads": [{"table": "cultural_events", "columns": null}], "writes": [{"table": "regular_order_products", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table smart_cities --target-dir /tmp/land\n", "labels": {"reads": [{"table": "smart_cities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"tracks\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ai_safety_incidents\");\n", "labels": {"reads": [{"table": "tracks", "columns": null}], "writes": [{"table": "ai_safety_incidents", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO circuits SELECT last_checkup_date, manufacturerid, station_name FROM providers WHERE last_checkup_date > 383\")\n", "labels": {"reads": [{"table": "providers", "columns": ["last_checkup_date", "manufacturerid", "station_name"]}], "writes": [{"table": "circuits", "columns": ["last_checkup_date", "manufacturerid", "station_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"militarybases\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "militarybases", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "workers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"satellite_missions_large\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"market_trends\")\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": null}], "writes": [{"table": "market_trends", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO healthydelights SELECT fare_date, date_and_date, appointment_duration FROM new_schedules WHERE fare_date > 460\");\n", "labels": {"reads": [{"table": "new_schedules", "columns": ["fare_date", "date_and_date", "appointment_duration"]}], "writes": [{"table": "healthydelights", "columns": ["fare_date", "date_and_date", "appointment_duration"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM customer_policies\"\n", "labels": {"reads": [{"table": "customer_policies", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT bname, item_id FROM mappinglengths LIMIT 170\")\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO city_waste_generation SELECT exhibition_name, studio FROM inclusive_housing WHERE exhibition_name > 238\")\n", "labels": {"reads": [{"table": "mappinglengths", "columns": ["bname", "item_id"]}, {"table": "inclusive_housing", "columns": ["exhibition_name", "studio"]}], "writes": [{"table": "city_waste_generation", "columns": ["exhibition_name", "studio"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws.dws_inventory_hourly SELECT * FROM legacy\ncur.execute(\"SELECT organisation_details, classroom FROM station_emergencies LIMIT 395\")\n", "labels": {"reads": [{"table": "station_emergencies", "columns": ["organisation_details", "classroom"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO mart.inventory_hourly SELECT news_outlet, flag FROM complaints WHERE news_outlet > 437\")\n", "labels": {"reads": [{"table": "complaints", "columns": ["news_outlet", "flag"]}], "writes": [{"table": "mart.inventory_hourly", "columns": ["news_outlet", "flag"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"safetyorgs\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "safetyorgs", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd.dwd_device_log_delta\");\ndf.write().mode(\"overwrite\").saveAsTable(\"player_sessions\");\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": null}], "writes": [{"table": "player_sessions", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO aircraftsquadrons SELECT postal_code, donationyear, commission_pct, vulnerability_score FROM bridges WHERE postal_code > 229\"], check=True)\n", "labels": {"reads": [{"table": "bridges", "columns": ["postal_code", "donationyear", "commission_pct", "vulnerability_score"]}], "writes": [{"table": "aircraftsquadrons", "columns": ["postal_code", "donationyear", "commission_pct", "vulnerability_score"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_practices\").toPandas()\ndf[[\"policyholderid\", \"co_id\"]].to_sql(\"government.city\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_practices", "columns": null}], "writes": [{"table": "government.city", "columns": ["policyholderid", "co_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO user_workouts_march SELECT * FROM legacy\nspark.sql(\"INSERT INTO book SELECT grantid, founded, last_name FROM labor_statistics WHERE grantid > 190\")\n", "labels": {"reads": [{"table": "labor_statistics", "columns": ["grantid", "founded", "last_name"]}], "writes": [{"table": "book", "columns": ["grantid", "founded", "last_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sustainability SELECT duration_ms, cargoid, undergraduate, worker_count FROM criminalcases WHERE duration_ms > 117\"\n", "labels": {"reads": [{"table": "criminalcases", "columns": ["duration_ms", "cargoid", "undergraduate", "worker_count"]}], "writes": [{"table": "sustainability", "columns": ["duration_ms", "cargoid", "undergraduate", "worker_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table paris_real_estate --target-dir /tmp/land\n", "labels": {"reads": [{"table": "paris_real_estate", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO contractorsales SELECT provider_id, therapy_session FROM discount_coupons WHERE provider_id > 202\"\n", "labels": {"reads": [{"table": "discount_coupons", "columns": ["provider_id", "therapy_session"]}], "writes": [{"table": "contractorsales", "columns": ["provider_id", "therapy_session"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table labor_hours --target-dir /tmp/land\n", "labels": {"reads": [{"table": "labor_hours", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"campaigns_2023\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "campaigns_2023", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT userid, base_name FROM manufacturingplants\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"clinics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "manufacturingplants", "columns": ["userid", "base_name"]}], "writes": [{"table": "clinics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw_member_point_full\").toPandas()\ndf[[\"date_joined_staff\", \"state\"]].to_sql(\"food_production\", engine, index=False)\n", "labels": {"reads": [{"table": "dw_member_point_full", "columns": null}], "writes": [{"table": "food_production", "columns": ["date_joined_staff", "state"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"heart_rate_data\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"transport\")\n", "labels": {"reads": [{"table": "heart_rate_data", "columns": null}], "writes": [{"table": "transport", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"game_sessions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "game_sessions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ods_cart_item_df SELECT customerid, hometown, unitsperweek FROM defense_projects_sales WHERE customerid > 346\"\n", "labels": {"reads": [{"table": "defense_projects_sales", "columns": ["customerid", "hometown", "unitsperweek"]}], "writes": [{"table": "ods_cart_item_df", "columns": ["customerid", "hometown", "unitsperweek"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO plays_games SELECT event_type_id, artifactid, license_type FROM vessel_performance WHERE event_type_id > 500\"\n", "labels": {"reads": [{"table": "vessel_performance", "columns": ["event_type_id", "artifactid", "license_type"]}], "writes": [{"table": "plays_games", "columns": ["event_type_id", "artifactid", "license_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contract_end, played FROM shelters LIMIT 282\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO socialimpactinvestments SELECT response_type, total_amount_purchased FROM ads.ads_vendors_hourly WHERE response_type > 315\")\n", "labels": {"reads": [{"table": "shelters", "columns": ["contract_end", "played"]}, {"table": "ads.ads_vendors_hourly", "columns": ["response_type", "total_amount_purchased"]}], "writes": [{"table": "socialimpactinvestments", "columns": ["response_type", "total_amount_purchased"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO document_types SELECT a.menu_item, b.dataset FROM pipelines a JOIN country_renewable_energy b ON a.request_date = b.request_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "pipelines", "columns": null}, {"table": "country_renewable_energy", "columns": null}], "writes": [{"table": "document_types", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ota_revenue\").where(\"dt = current_date()\").writeTo(\"artistsdemographics\").append()\n", "labels": {"reads": [{"table": "ota_revenue", "columns": null}], "writes": [{"table": "artistsdemographics", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT productlaunchdate, violation_id FROM upgrades\", engine)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"dams\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "upgrades", "columns": ["productlaunchdate", "violation_id"]}], "writes": [{"table": "dams", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO patient SELECT trip_duration, menuitemid, investors, rebounds FROM manufacturer WHERE trip_duration > 422\")\n", "labels": {"reads": [{"table": "manufacturer", "columns": ["trip_duration", "menuitemid", "investors", "rebounds"]}], "writes": [{"table": "patient", "columns": ["trip_duration", "menuitemid", "investors", "rebounds"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT founding_location, city_population FROM part_faults\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"convictions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "part_faults", "columns": ["founding_location", "city_population"]}], "writes": [{"table": "convictions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"climate_adaptation_re\");\ndf.write().mode(\"overwrite\").saveAsTable(\"home_game\");\n", "labels": {"reads": [{"table": "climate_adaptation_re", "columns": null}], "writes": [{"table": "home_game", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ucl_top10 SELECT a.driverid, b.dispensary_name FROM soccer_goals a JOIN dw.dw_sessions_delta b ON a.treatment_year = b.treatment_year\"\n", "labels": {"reads": [{"table": "soccer_goals", "columns": null}, {"table": "dw.dw_sessions_delta", "columns": null}], "writes": [{"table": "ucl_top10", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"animal_rehab\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "animal_rehab", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model immunization depends on busmaintenance\ndbt build -s immunization --vars '{\"source_table\":\"busmaintenance\"}'\n", "labels": {"reads": [{"table": "busmaintenance", "columns": null}], "writes": [{"table": "immunization", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO performances SELECT a.birthday, b.policyname FROM mart.campaigns_full a JOIN sales_region b ON a.drug = b.drug\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mart.campaigns_full", "columns": null}, {"table": "sales_region", "columns": null}], "writes": [{"table": "performances", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"restorative_justice_center\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"stg.stg_campaigns\")\n", "labels": {"reads": [{"table": "restorative_justice_center", "columns": null}], "writes": [{"table": "stg.stg_campaigns", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO game_scores SELECT emp_hiredate, framework_id, airport_name FROM mexico_regions WHERE emp_hiredate > 479\"\n", "labels": {"reads": [{"table": "mexico_regions", "columns": ["emp_hiredate", "framework_id", "airport_name"]}], "writes": [{"table": "game_scores", "columns": ["emp_hiredate", "framework_id", "airport_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT courses, money_requested FROM us_platforms\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"convictions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "us_platforms", "columns": ["courses", "money_requested"]}], "writes": [{"table": "convictions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tours (driverid, hardware_model_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tours", "columns": ["driverid", "hardware_model_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 424;\nSQL\n", "labels": {"reads": [{"table": "sustainability", "columns": ["departmentname", "partnership_id"]}, {"table": "bi.bi_inventory", "columns": ["incident_description", "role_description", "invoice_number"]}], "writes": [{"table": "climate_finance", "columns": ["incident_description", "role_description", "invoice_number"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cargo_weight, age_group_id FROM shipments LIMIT 284\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "shipments", "columns": ["cargo_weight", "age_group_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"trains\").where(\"dt = current_date()\").writeTo(\"game_results\").append()\n", "labels": {"reads": [{"table": "trains", "columns": null}], "writes": [{"table": "game_results", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO department SELECT * FROM legacy\ncur.execute(\"SELECT enable_third_party_ads, animal_type FROM renewable_power LIMIT 167\")\n", "labels": {"reads": [{"table": "renewable_power", "columns": ["enable_third_party_ads", "animal_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fairness_scores\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"apartments\")\n", "labels": {"reads": [{"table": "fairness_scores", "columns": null}], "writes": [{"table": "apartments", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO gene SELECT vehicle_name, style FROM platformh WHERE vehicle_name > 164\"\n", "labels": {"reads": [{"table": "platformh", "columns": ["vehicle_name", "style"]}], "writes": [{"table": "gene", "columns": ["vehicle_name", "style"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"all_programs\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "all_programs", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"carbon_offset_south_america\").toPandas()\ndf[[\"actid\", \"song_name\"]].to_sql(\"clothingsales\", engine, index=False)\n", "labels": {"reads": [{"table": "carbon_offset_south_america", "columns": null}], "writes": [{"table": "clothingsales", "columns": ["actid", "song_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"stg.stg_risk_score\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stay\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tokyo_motor_show\")\n", "labels": {"reads": [{"table": "stay", "columns": null}], "writes": [{"table": "tokyo_motor_show", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO postseason SELECT authors, date_of_enrolment, dept_code FROM urban_agriculture_initiatives WHERE authors > 236\"\n", "labels": {"reads": [{"table": "urban_agriculture_initiatives", "columns": ["authors", "date_of_enrolment", "dept_code"]}], "writes": [{"table": "postseason", "columns": ["authors", "date_of_enrolment", "dept_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table distributors --target-dir /tmp/land\n", "labels": {"reads": [{"table": "distributors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO mining.company SELECT updated_at, handling_date FROM opendatainitiatives WHERE updated_at > 114\");\n", "labels": {"reads": [{"table": "opendatainitiatives", "columns": ["updated_at", "handling_date"]}], "writes": [{"table": "mining.company", "columns": ["updated_at", "handling_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM urban_transportation\"\n", "labels": {"reads": [{"table": "urban_transportation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"staff_department_assignments\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ethics_violations\")\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": null}], "writes": [{"table": "ethics_violations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO iot_sensors SELECT * FROM legacy\nspark.sql(\"INSERT INTO hotel_chains SELECT card_id, element_id FROM list WHERE card_id > 317\")\n", "labels": {"reads": [{"table": "list", "columns": ["card_id", "element_id"]}], "writes": [{"table": "hotel_chains", "columns": ["card_id", "element_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"channel\").where(\"dt = current_date()\").writeTo(\"regulatory_frameworks\").append()\n", "labels": {"reads": [{"table": "channel", "columns": null}], "writes": [{"table": "regulatory_frameworks", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO organic_farms SELECT museum_name, scoreid, available_yn, tourist_details FROM member_of_club WHERE museum_name > 488\");\n", "labels": {"reads": [{"table": "member_of_club", "columns": ["museum_name", "scoreid", "available_yn", "tourist_details"]}], "writes": [{"table": "organic_farms", "columns": ["museum_name", "scoreid", "available_yn", "tourist_details"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO philadelphia_police_emergencies (supplier_id, date_of_birth) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "philadelphia_police_emergencies", "columns": ["supplier_id", "date_of_birth"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO nyc_subway (segment_id, institution) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "nyc_subway", "columns": ["segment_id", "institution"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT restaurantid, trial_name FROM public_transportation_sydney LIMIT 434\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": ["restaurantid", "trial_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO tickets_3 SELECT emp_id, num_beds, is_deforested FROM organic_farms WHERE emp_id > 326\")\n", "labels": {"reads": [{"table": "organic_farms", "columns": ["emp_id", "num_beds", "is_deforested"]}], "writes": [{"table": "tickets_3", "columns": ["emp_id", "num_beds", "is_deforested"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_risk_score_delta\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"league\")\n", "labels": {"reads": [{"table": "ods.ods_risk_score_delta", "columns": null}], "writes": [{"table": "league", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model refugees depends on animal_rehab\ndbt run --models refugees --vars '{\"src\":\"animal_rehab\"}'\n", "labels": {"reads": [{"table": "animal_rehab", "columns": null}], "writes": [{"table": "refugees", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO teams SELECT a.porphyria, b.party FROM volunteer_registration a JOIN socially_responsible_loans b ON a.success = b.success\"\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": null}, {"table": "socially_responsible_loans", "columns": null}], "writes": [{"table": "teams", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"game_sessions\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"autonomousvehicleaccidents\")\n", "labels": {"reads": [{"table": "game_sessions", "columns": null}], "writes": [{"table": "autonomousvehicleaccidents", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.co2_reduction_tons > 179).all()\n# src table: organizations\nengine.execute(\"INSERT INTO coralreefs SELECT * FROM organizations\")\n", "labels": {"reads": [{"table": "organizations", "columns": null}], "writes": [{"table": "coralreefs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO aquaculture_farms SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO smartcityprojects SELECT alert_id, program_name, pixels FROM bias_categories WHERE alert_id > 172\");\n", "labels": {"reads": [{"table": "bias_categories", "columns": ["alert_id", "program_name", "pixels"]}], "writes": [{"table": "smartcityprojects", "columns": ["alert_id", "program_name", "pixels"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model music_festival depends on biosensor.patents\ndbt run --select music_festival --vars '{\"source_table\":\"biosensor.patents\"}'\n", "labels": {"reads": [{"table": "biosensor.patents", "columns": null}], "writes": [{"table": "music_festival", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.mart_refunds_di\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mart.mart_refunds_di", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO endowment SELECT propertyid, partitionid, habitat_name FROM contract_negotiations_un WHERE propertyid > 492\"\n", "labels": {"reads": [{"table": "contract_negotiations_un", "columns": ["propertyid", "partitionid", "habitat_name"]}], "writes": [{"table": "endowment", "columns": ["propertyid", "partitionid", "habitat_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stg.coupon_use_delta SELECT bank_id, borough, did, project_date FROM militarypersonnel WHERE bank_id > 430\"\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": ["bank_id", "borough", "did", "project_date"]}], "writes": [{"table": "stg.coupon_use_delta", "columns": ["bank_id", "borough", "did", "project_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_input(ctx, \"ads\")\nsink_to_output(df, \"dw.dw_orders_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads", "columns": null}], "writes": [{"table": "dw.dw_orders_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO open_pedagogy_courses SELECT * FROM legacy\nspark.sql(\"INSERT INTO ods_shipments_df SELECT production_value, leadershiptraining, prof_high_degree FROM therapy WHERE production_value > 52\")\n", "labels": {"reads": [{"table": "therapy", "columns": ["production_value", "leadershiptraining", "prof_high_degree"]}], "writes": [{"table": "ods_shipments_df", "columns": ["production_value", "leadershiptraining", "prof_high_degree"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO degrees SELECT endtime, spacecraft_id, observation_id, artistname FROM mart_shipments_full WHERE endtime > 102\")\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": ["endtime", "spacecraft_id", "observation_id", "artistname"]}], "writes": [{"table": "degrees", "columns": ["endtime", "spacecraft_id", "observation_id", "artistname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"ads_sessions_di\")\nsrc.write.insertInto(\"healthcare_access_v2\", overwrite=True)\n", "labels": {"reads": [{"table": "ads_sessions_di", "columns": null}], "writes": [{"table": "healthcare_access_v2", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO menu_items SELECT * FROM legacy\nspark.sql(\"INSERT INTO dws_coupon_use_df SELECT vendorname, room_number, pet_age FROM renewable_projects WHERE vendorname > 229\")\n", "labels": {"reads": [{"table": "renewable_projects", "columns": ["vendorname", "room_number", "pet_age"]}], "writes": [{"table": "dws_coupon_use_df", "columns": ["vendorname", "room_number", "pet_age"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT treatment_name, round_date FROM customers_policies\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"emergency_calls\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customers_policies", "columns": ["treatment_name", "round_date"]}], "writes": [{"table": "emergency_calls", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO mediterranean_salinity SELECT trial_id, stayid, state_province FROM video_content WHERE trial_id > 341\"\n", "labels": {"reads": [{"table": "video_content", "columns": ["trial_id", "stayid", "state_province"]}], "writes": [{"table": "mediterranean_salinity", "columns": ["trial_id", "stayid", "state_province"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tourism_activities\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"teams_mascots\")\n", "labels": {"reads": [{"table": "tourism_activities", "columns": null}], "writes": [{"table": "teams_mascots", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT stock, program_expenses FROM stg.coupon_use_delta\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"genderdistribution\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stg.coupon_use_delta", "columns": ["stock", "program_expenses"]}], "writes": [{"table": "genderdistribution", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"continents\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"tickets_3\")\n", "labels": {"reads": [{"table": "continents", "columns": null}], "writes": [{"table": "tickets_3", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stores_2\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tryout\")\n", "labels": {"reads": [{"table": "stores_2", "columns": null}], "writes": [{"table": "tryout", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO inst (fleet_id, animal) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "inst", "columns": ["fleet_id", "animal"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO person SELECT a.total_employees, b.actid FROM tourist_attraction_features a JOIN nba_games b ON a.facultyid = b.facultyid\"\n", "labels": {"reads": [{"table": "tourist_attraction_features", "columns": null}, {"table": "nba_games", "columns": null}], "writes": [{"table": "person", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"seafood\");\ndf.write().mode(\"overwrite\").saveAsTable(\"donationprograms\");\n", "labels": {"reads": [{"table": "seafood", "columns": null}], "writes": [{"table": "donationprograms", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"biotech_startups\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "biotech_startups", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT excavation_site, prereq_id FROM customer_transactions LIMIT 40\")\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO workshops SELECT participation_id, personnel, projecttype, mental_health_rating FROM communityengagement WHERE participation_id > 371\")\n", "labels": {"reads": [{"table": "customer_transactions", "columns": ["excavation_site", "prereq_id"]}, {"table": "communityengagement", "columns": ["participation_id", "personnel", "projecttype", "mental_health_rating"]}], "writes": [{"table": "workshops", "columns": ["participation_id", "personnel", "projecttype", "mental_health_rating"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table cargo_handling --target-dir /tmp/land\n", "labels": {"reads": [{"table": "cargo_handling", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"providers\")\nsrc.write.insertInto(\"tracklists\", overwrite=True)\n", "labels": {"reads": [{"table": "providers", "columns": null}], "writes": [{"table": "tracklists", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO indian_ocean_fishingvessels SELECT customerid, start_time, invoice_details, dock_status FROM tech_workers_union WHERE customerid > 433\"\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": ["customerid", "start_time", "invoice_details", "dock_status"]}], "writes": [{"table": "indian_ocean_fishingvessels", "columns": ["customerid", "start_time", "invoice_details", "dock_status"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table restorative_justice_3 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "restorative_justice_3", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO brands SELECT a.artwork, b.trade FROM professionals a JOIN artists b ON a.severity = b.severity\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "professionals", "columns": null}, {"table": "artists", "columns": null}], "writes": [{"table": "brands", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO productsafety SELECT offender_id, shipmentid, dish_type FROM donorprograms WHERE offender_id > 347\"\n", "labels": {"reads": [{"table": "donorprograms", "columns": ["offender_id", "shipmentid", "dish_type"]}], "writes": [{"table": "productsafety", "columns": ["offender_id", "shipmentid", "dish_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"humanitarian_aid\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"militaryinnovations\")\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": null}], "writes": [{"table": "militaryinnovations", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 251;\nSQL\n", "labels": {"reads": [{"table": "trust", "columns": ["event_name", "date_closed"]}, {"table": "stellar_transactions", "columns": ["crs_credit", "artifact_type", "minister", "materialid"]}], "writes": [{"table": "military_tech", "columns": ["crs_credit", "artifact_type", "minister", "materialid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads_payments_di\").toPandas()\ndf[[\"donation_id\", \"document_structure_code\"]].to_sql(\"investments\", engine, index=False)\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": null}], "writes": [{"table": "investments", "columns": ["donation_id", "document_structure_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO student_tests_taken SELECT played, date_of_latest_logon, menuitemid, cityname FROM community_centers WHERE played > 384\")\n", "labels": {"reads": [{"table": "community_centers", "columns": ["played", "date_of_latest_logon", "menuitemid", "cityname"]}], "writes": [{"table": "student_tests_taken", "columns": ["played", "date_of_latest_logon", "menuitemid", "cityname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO incidents SELECT organisation_id, dept_store_chain_name, personal_name FROM languages WHERE organisation_id > 208\")\n", "labels": {"reads": [{"table": "languages", "columns": ["organisation_id", "dept_store_chain_name", "personal_name"]}], "writes": [{"table": "incidents", "columns": ["organisation_id", "dept_store_chain_name", "personal_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"incident\").where(\"dt = current_date()\").writeTo(\"cultivators\").append()\n", "labels": {"reads": [{"table": "incident", "columns": null}], "writes": [{"table": "cultivators", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO policyholders (excavation_site_id, mappingname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "policyholders", "columns": ["excavation_site_id", "mappingname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"investor_activities\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"temperaturerecords\")\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": [{"table": "temperaturerecords", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"outcomes\")\nsrc.write.insertInto(\"funding_rounds\", overwrite=True)\n", "labels": {"reads": [{"table": "outcomes", "columns": null}], "writes": [{"table": "funding_rounds", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table restorative_justice_sentences --columns incidents,location --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "restorative_justice_sentences", "columns": ["incidents", "location"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"temperaturehistory\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"roles\")\n", "labels": {"reads": [{"table": "temperaturehistory", "columns": null}], "writes": [{"table": "roles", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO al_jazeera_data SELECT * FROM legacy\ncur.execute(\"SELECT attraction_type_code, check_in_date FROM ods_sessions LIMIT 73\")\n", "labels": {"reads": [{"table": "ods_sessions", "columns": ["attraction_type_code", "check_in_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM spending\", conn)\ndf.to_sql(\"ads.inventory_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "spending", "columns": null}], "writes": [{"table": "ads.inventory_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_input(ctx, \"endowment\")\nsave_to_sink(df, \"soccer_goals\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "endowment", "columns": null}], "writes": [{"table": "soccer_goals", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cultural_competency (unitsperweek, pediatrician_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cultural_competency", "columns": ["unitsperweek", "pediatrician_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO stg.refunds_daily SELECT omim, transaction_value, payment_method, program_name FROM temperature_data WHERE omim > 465\");\n", "labels": {"reads": [{"table": "temperature_data", "columns": ["omim", "transaction_value", "payment_method", "program_name"]}], "writes": [{"table": "stg.refunds_daily", "columns": ["omim", "transaction_value", "payment_method", "program_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fish_stock\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "fish_stock", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO city_budgets SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO races SELECT * FROM legacy\ncur.execute(\"SELECT half, teacher_id FROM billstatus LIMIT 221\")\n", "labels": {"reads": [{"table": "billstatus", "columns": ["half", "teacher_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"shared_escooters\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "shared_escooters", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO africa_projects SELECT a.time_of_purchase, b.venue FROM waste_generation_metrics a JOIN member_of_club b ON a.issue_id = b.issue_id\"\n", "labels": {"reads": [{"table": "waste_generation_metrics", "columns": null}, {"table": "member_of_club", "columns": null}], "writes": [{"table": "africa_projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO mart.mart_users_delta SELECT initiative_name, pollutant_type, pname, transaction_id FROM threat_intelligence_budget WHERE initiative_name > 89\")\n", "labels": {"reads": [{"table": "threat_intelligence_budget", "columns": ["initiative_name", "pollutant_type", "pname", "transaction_id"]}], "writes": [{"table": "mart.mart_users_delta", "columns": ["initiative_name", "pollutant_type", "pname", "transaction_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO happy_hour SELECT assessmentid, vehicleid, tripdatetime FROM vehicle_prices WHERE assessmentid > 407\"\n", "labels": {"reads": [{"table": "vehicle_prices", "columns": ["assessmentid", "vehicleid", "tripdatetime"]}], "writes": [{"table": "happy_hour", "columns": ["assessmentid", "vehicleid", "tripdatetime"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dwd.dwd_orders_daily\"\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO technology_access (dissolved_oxygen, postal_code) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "technology_access", "columns": ["dissolved_oxygen", "postal_code"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO stg.stg_campaigns SELECT system_id, year_deforested, order_item_id, subscription_start_date FROM teacher_pd_hours WHERE system_id > 80\");\n", "labels": {"reads": [{"table": "teacher_pd_hours", "columns": ["system_id", "year_deforested", "order_item_id", "subscription_start_date"]}], "writes": [{"table": "stg.stg_campaigns", "columns": ["system_id", "year_deforested", "order_item_id", "subscription_start_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO reservations SELECT zipcode, experienceid FROM party_events WHERE zipcode > 496\"], check=True)\n", "labels": {"reads": [{"table": "party_events", "columns": ["zipcode", "experienceid"]}], "writes": [{"table": "reservations", "columns": ["zipcode", "experienceid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"browser\");\ndf.write().mode(\"overwrite\").saveAsTable(\"features\");\n", "labels": {"reads": [{"table": "browser", "columns": null}], "writes": [{"table": "features", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO on_call SELECT * FROM legacy\ncur.execute(\"SELECT paritystatus, date_account_opened FROM election LIMIT 374\")\n", "labels": {"reads": [{"table": "election", "columns": ["paritystatus", "date_account_opened"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM paris_real_estate\", conn)\ndf.to_sql(\"asset_parts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "paris_real_estate", "columns": null}], "writes": [{"table": "asset_parts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO authors SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.funding_round_id > 419).all()\n# src table: chemical_processes\nengine.execute(\"INSERT INTO construction_labor SELECT * FROM chemical_processes\")\n", "labels": {"reads": [{"table": "chemical_processes", "columns": null}], "writes": [{"table": "construction_labor", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"classicgame\");\ndf.write().mode(\"overwrite\").saveAsTable(\"social_good_education\");\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": [{"table": "social_good_education", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT route_name, meter_200 FROM mart.mart_device_log LIMIT 474\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ods.ods_exposure_delta SELECT mealname, transit_passengers, field_id FROM artpieces WHERE mealname > 260\")\n", "labels": {"reads": [{"table": "mart.mart_device_log", "columns": ["route_name", "meter_200"]}, {"table": "artpieces", "columns": ["mealname", "transit_passengers", "field_id"]}], "writes": [{"table": "ods.ods_exposure_delta", "columns": ["mealname", "transit_passengers", "field_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO customers_cards SELECT gametype, budgeted, nurse FROM ocean_acidification WHERE gametype > 363\")\n", "labels": {"reads": [{"table": "ocean_acidification", "columns": ["gametype", "budgeted", "nurse"]}], "writes": [{"table": "customers_cards", "columns": ["gametype", "budgeted", "nurse"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO rainfall_data SELECT enroll_grade, missions, followers, publication_id FROM influencers WHERE enroll_grade > 29\"\n", "labels": {"reads": [{"table": "influencers", "columns": ["enroll_grade", "missions", "followers", "publication_id"]}], "writes": [{"table": "rainfall_data", "columns": ["enroll_grade", "missions", "followers", "publication_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO faculty SELECT eventattendance, rural_area, topic, rank FROM match_result WHERE eventattendance > 214\")\n", "labels": {"reads": [{"table": "match_result", "columns": ["eventattendance", "rural_area", "topic", "rank"]}], "writes": [{"table": "faculty", "columns": ["eventattendance", "rural_area", "topic", "rank"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"green_buildings_us\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "green_buildings_us", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cerium_production SELECT fleet_id, amountdonated, avg_depth, club_name FROM textileworkers WHERE fleet_id > 83\"\n", "labels": {"reads": [{"table": "textileworkers", "columns": ["fleet_id", "amountdonated", "avg_depth", "club_name"]}], "writes": [{"table": "cerium_production", "columns": ["fleet_id", "amountdonated", "avg_depth", "club_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ads.refunds_delta\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO carbon_pricing (savingsid, built) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "carbon_pricing", "columns": ["savingsid", "built"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"weapons\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "weapons", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO vaccine_administered SELECT played, region, shelter_id FROM vehicle_data WHERE played > 66\")\n", "labels": {"reads": [{"table": "vehicle_data", "columns": ["played", "region", "shelter_id"]}], "writes": [{"table": "vaccine_administered", "columns": ["played", "region", "shelter_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"useracct\");\ndf.write().mode(\"overwrite\").saveAsTable(\"traditionalarts\");\n", "labels": {"reads": [{"table": "useracct", "columns": null}], "writes": [{"table": "traditionalarts", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT years_played, communityid FROM customers_cards\", engine)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"circular_economy_initiatives\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customers_cards", "columns": ["years_played", "communityid"]}], "writes": [{"table": "circular_economy_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ads.ads_events_df SELECT text_of_notes, matchdate, policy_number FROM dwd.vendors WHERE text_of_notes > 196\")\n", "labels": {"reads": [{"table": "dwd.vendors", "columns": ["text_of_notes", "matchdate", "policy_number"]}], "writes": [{"table": "ads.ads_events_df", "columns": ["text_of_notes", "matchdate", "policy_number"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.stg_users_full SELECT a.video_id, b.strainname FROM ods.ods_campaigns_hourly a JOIN station_company b ON a.currency = b.currency\"\n", "labels": {"reads": [{"table": "ods.ods_campaigns_hourly", "columns": null}, {"table": "station_company", "columns": null}], "writes": [{"table": "stg.stg_users_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"paris_train\")\nupsert_to_store(df, \"vehicle_registrations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "paris_train", "columns": null}], "writes": [{"table": "vehicle_registrations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO indian_ocean_wells SELECT a.billing_amount, b.aid_name FROM stg.stg_clicks_delta a JOIN bi.clicks_hourly b ON a.activity_id = b.activity_id\"\n", "labels": {"reads": [{"table": "stg.stg_clicks_delta", "columns": null}, {"table": "bi.clicks_hourly", "columns": null}], "writes": [{"table": "indian_ocean_wells", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM transportation_per_country\", conn)\ndf.to_sql(\"customer_month\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": null}], "writes": [{"table": "customer_month", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM problem_log\"\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO rural_infrastructure SELECT total_points, contract_type, energy_star_rating, bedroom_count FROM ref_colors WHERE total_points > 16\")\n", "labels": {"reads": [{"table": "ref_colors", "columns": ["total_points", "contract_type", "energy_star_rating", "bedroom_count"]}], "writes": [{"table": "rural_infrastructure", "columns": ["total_points", "contract_type", "energy_star_rating", "bedroom_count"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dno, material_date FROM price_data LIMIT 387\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "price_data", "columns": ["dno", "material_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"buildings\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "buildings", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"regulatory_frameworks\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"stores_2\")\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": null}], "writes": [{"table": "stores_2", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO school SELECT * FROM legacy\ncur.execute(\"SELECT co2_offset_amount, operation_type FROM wrestler LIMIT 220\")\n", "labels": {"reads": [{"table": "wrestler", "columns": ["co2_offset_amount", "operation_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO policyanalysis SELECT a.approval_date, b.inclusive_housing_policy FROM settlements a JOIN renewable_energy_investments b ON a.end_station_id = b.end_station_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "settlements", "columns": null}, {"table": "renewable_energy_investments", "columns": null}], "writes": [{"table": "policyanalysis", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO round (capacity, vesselid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "round", "columns": ["capacity", "vesselid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"circular_supply_chain_products\");\ndf.write().mode(\"overwrite\").saveAsTable(\"vendorfabrics\");\n", "labels": {"reads": [{"table": "circular_supply_chain_products", "columns": null}], "writes": [{"table": "vendorfabrics", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO bi.inventory_delta SELECT num_schools, orgname, num_transactions, minename FROM shrimp_farms WHERE num_schools > 353\");\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": ["num_schools", "orgname", "num_transactions", "minename"]}], "writes": [{"table": "bi.inventory_delta", "columns": ["num_schools", "orgname", "num_transactions", "minename"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO recyclednylongarments SELECT a.safetytestdate, b.musical_id FROM crop_yield a JOIN na_schema.hospitals b ON a.cargo_weight = b.cargo_weight\"\n", "labels": {"reads": [{"table": "crop_yield", "columns": null}, {"table": "na_schema.hospitals", "columns": null}], "writes": [{"table": "recyclednylongarments", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ods.ods_coupon_use_delta --columns sighting_id,thing_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ods.ods_coupon_use_delta", "columns": ["sighting_id", "thing_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO paper_data SELECT eventattendance, lot_id FROM crops WHERE eventattendance > 217\");\n", "labels": {"reads": [{"table": "crops", "columns": ["eventattendance", "lot_id"]}], "writes": [{"table": "paper_data", "columns": ["eventattendance", "lot_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO heritagesites SELECT reviews, environmental_impact_score, funding_source FROM council_tax WHERE reviews > 418\")\n", "labels": {"reads": [{"table": "council_tax", "columns": ["reviews", "environmental_impact_score", "funding_source"]}], "writes": [{"table": "heritagesites", "columns": ["reviews", "environmental_impact_score", "funding_source"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws.dws_member_point_di SELECT * FROM legacy\ncur.execute(\"SELECT exoplanet, salinity FROM economic_diversification_efforts LIMIT 32\")\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": ["exoplanet", "salinity"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"shariah_compliant_products\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"military_bases\")\n", "labels": {"reads": [{"table": "shariah_compliant_products", "columns": null}], "writes": [{"table": "military_bases", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO skincareinventory SELECT actual_delivery_date, cid, exploited FROM fair_trade_suppliers WHERE actual_delivery_date > 221\"\n", "labels": {"reads": [{"table": "fair_trade_suppliers", "columns": ["actual_delivery_date", "cid", "exploited"]}], "writes": [{"table": "skincareinventory", "columns": ["actual_delivery_date", "cid", "exploited"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO labor_cost SELECT * FROM legacy\nspark.sql(\"INSERT INTO skincareinventory SELECT customer_country, invoice_date, animal_id FROM dwd_coupon_use_hourly WHERE customer_country > 399\")\n", "labels": {"reads": [{"table": "dwd_coupon_use_hourly", "columns": ["customer_country", "invoice_date", "animal_id"]}], "writes": [{"table": "skincareinventory", "columns": ["customer_country", "invoice_date", "animal_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart_cart_item_di (budgeted, professional_development_programs) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart_cart_item_di", "columns": ["budgeted", "professional_development_programs"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO fish_farms (artworkid, international_passengers) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "fish_farms", "columns": ["artworkid", "international_passengers"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO tweets SELECT stat_type, source_system_code FROM communityengagements WHERE stat_type > 457\"\n", "labels": {"reads": [{"table": "communityengagements", "columns": ["stat_type", "source_system_code"]}], "writes": [{"table": "tweets", "columns": ["stat_type", "source_system_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO claims_processing_stages SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"singer_in_concert\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT billingcountry, asessment_outcome_code FROM member_details\", engine)\nimport logging\ndf.to_sql(\"defense_diplomacy\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "member_details", "columns": ["billingcountry", "asessment_outcome_code"]}], "writes": [{"table": "defense_diplomacy", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO menu_vendors SELECT * FROM legacy\ncur.execute(\"SELECT grade, shelter_id FROM shared_ebikes LIMIT 343\")\n", "labels": {"reads": [{"table": "shared_ebikes", "columns": ["grade", "shelter_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"volunteer_hours\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"students\")\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": null}], "writes": [{"table": "students", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 387;\nSQL\n", "labels": {"reads": [{"table": "fund_investments", "columns": ["cases_handled", "target_name"]}, {"table": "playergamehistory", "columns": ["other_account_details", "election_cycle", "enddate", "advocate_id"]}], "writes": [{"table": "trafficviolations", "columns": ["other_account_details", "election_cycle", "enddate", "advocate_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"emergency_calls\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "emergency_calls", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 181;\nSQL\n", "labels": {"reads": [{"table": "product_info", "columns": ["daily_consumption", "card_type_code"]}, {"table": "decentralized_applications", "columns": ["end_time", "artifacttype"]}], "writes": [{"table": "ods.ods_events_daily", "columns": ["end_time", "artifacttype"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"transportation\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"vehicle_registrations\")\n", "labels": {"reads": [{"table": "transportation", "columns": null}], "writes": [{"table": "vehicle_registrations", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO residents_services (handling_date, skill_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "residents_services", "columns": ["handling_date", "skill_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd.vendors\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dwd.vendors", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tickets_3\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"labor_statistics\")\n", "labels": {"reads": [{"table": "tickets_3", "columns": null}], "writes": [{"table": "labor_statistics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.acidification_level > 270).all()\n# src table: public.police_calls\nengine.execute(\"INSERT INTO tencel_sources SELECT * FROM public.police_calls\")\n", "labels": {"reads": [{"table": "public.police_calls", "columns": null}], "writes": [{"table": "tencel_sources", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"organizations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"battery_storage\")\n", "labels": {"reads": [{"table": "organizations", "columns": null}], "writes": [{"table": "battery_storage", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM circularsupplychain\"\n", "labels": {"reads": [{"table": "circularsupplychain", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO recycling_rates_state (attraction_name, sector) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "recycling_rates_state", "columns": ["attraction_name", "sector"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO mart_exposure_di SELECT element_id, judge_id FROM dws.dws_member_point_df WHERE element_id > 33\")\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": ["element_id", "judge_id"]}], "writes": [{"table": "mart_exposure_di", "columns": ["element_id", "judge_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO support SELECT festival_id, election_cycle, ssn FROM farm_competition WHERE festival_id > 357\")\n", "labels": {"reads": [{"table": "farm_competition", "columns": ["festival_id", "election_cycle", "ssn"]}], "writes": [{"table": "support", "columns": ["festival_id", "election_cycle", "ssn"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO accommodations SELECT * FROM legacy\nspark.sql(\"INSERT INTO menu_item SELECT to_address, seating, program_name, clinic_name FROM west_providers WHERE to_address > 448\")\n", "labels": {"reads": [{"table": "west_providers", "columns": ["to_address", "seating", "program_name", "clinic_name"]}], "writes": [{"table": "menu_item", "columns": ["to_address", "seating", "program_name", "clinic_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO chemical_production_5 (year_join, delivery_time) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "chemical_production_5", "columns": ["year_join", "delivery_time"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO climatefinance SELECT violation_id, hashtags FROM ai_ethics_policies WHERE violation_id > 68\");\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": ["violation_id", "hashtags"]}], "writes": [{"table": "climatefinance", "columns": ["violation_id", "hashtags"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"crime_incidents\")\nupsert_to_sink(df, \"workerbuildings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "crime_incidents", "columns": null}], "writes": [{"table": "workerbuildings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 6;\nSQL\n", "labels": {"reads": [{"table": "criminal_cases", "columns": ["max_depth", "access_date"]}, {"table": "ref_calendar", "columns": ["watch_time", "is_operational", "star_rating_code"]}], "writes": [{"table": "manufacturers", "columns": ["watch_time", "is_operational", "star_rating_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT complete_date, room_count FROM mart.mart_users_df LIMIT 299\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "mart.mart_users_df", "columns": ["complete_date", "room_count"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.item > 106).all()\n# src table: camera_lens\nengine.execute(\"INSERT INTO phone_market SELECT * FROM camera_lens\")\n", "labels": {"reads": [{"table": "camera_lens", "columns": null}], "writes": [{"table": "phone_market", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"attorney_billing\");\ndf.write().mode(\"overwrite\").saveAsTable(\"exit_strategy\");\n", "labels": {"reads": [{"table": "attorney_billing", "columns": null}], "writes": [{"table": "exit_strategy", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gymc_members\").toPandas()\ndf[[\"classtype\", \"artworkname\"]].to_sql(\"events\", engine, index=False)\n", "labels": {"reads": [{"table": "gymc_members", "columns": null}], "writes": [{"table": "events", "columns": ["classtype", "artworkname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO traditionalarts SELECT grant_date, image_data, thing_id, longitude FROM product WHERE grant_date > 292\"\n", "labels": {"reads": [{"table": "product", "columns": ["grant_date", "image_data", "thing_id", "longitude"]}], "writes": [{"table": "traditionalarts", "columns": ["grant_date", "image_data", "thing_id", "longitude"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO courses SELECT college, rank_in_round, tier FROM canada_tech WHERE college > 237\"\n", "labels": {"reads": [{"table": "canada_tech", "columns": ["college", "rank_in_round", "tier"]}], "writes": [{"table": "courses", "columns": ["college", "rank_in_round", "tier"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"navalvessels\")\nsrc.write.insertInto(\"geological_survey\", overwrite=True)\n", "labels": {"reads": [{"table": "navalvessels", "columns": null}], "writes": [{"table": "geological_survey", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 62;\nSQL\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": ["issue_month", "fuelid"]}, {"table": "tech_accessibility_funding", "columns": ["frameworkcountry", "batting_average", "location_description"]}], "writes": [{"table": "satelliteimagery", "columns": ["frameworkcountry", "batting_average", "location_description"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"consumer_preference\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"genderdistribution\")\n", "labels": {"reads": [{"table": "consumer_preference", "columns": null}], "writes": [{"table": "genderdistribution", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT individual_first_name, crop_name FROM community_programs LIMIT 255\")\nrows = cur.fetchall()\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "community_programs", "columns": ["individual_first_name", "crop_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"match_result\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "match_result", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.volume_id > 186).all()\n# src table: infra_diversification\nengine.execute(\"INSERT INTO satellite_missions_large SELECT * FROM infra_diversification\")\n", "labels": {"reads": [{"table": "infra_diversification", "columns": null}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model highest_scores depends on wind_energy_projects\ndbt build --models highest_scores --vars 'source: wind_energy_projects'\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": null}], "writes": [{"table": "highest_scores", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO participants_in_events SELECT site, trackid FROM customer_payments WHERE site > 319\"\n", "labels": {"reads": [{"table": "customer_payments", "columns": ["site", "trackid"]}], "writes": [{"table": "participants_in_events", "columns": ["site", "trackid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO musical SELECT gender_diversity, host_country FROM mining_operations WHERE gender_diversity > 39\")\n", "labels": {"reads": [{"table": "mining_operations", "columns": ["gender_diversity", "host_country"]}], "writes": [{"table": "musical", "columns": ["gender_diversity", "host_country"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"carbon_emissions\").where(\"dt = current_date()\").writeTo(\"socially_responsible_loans\").append()\n", "labels": {"reads": [{"table": "carbon_emissions", "columns": null}], "writes": [{"table": "socially_responsible_loans", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"accessible_tech_categories\").toPandas()\ndf[[\"borough\", \"dormid\"]].to_sql(\"fossil_fuel_vehicles\", engine, index=False)\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": null}], "writes": [{"table": "fossil_fuel_vehicles", "columns": ["borough", "dormid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT train_type, incident_type_description FROM explainableai\", engine)\nimport logging\ndf.to_sql(\"space_programs\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "explainableai", "columns": ["train_type", "incident_type_description"]}], "writes": [{"table": "space_programs", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"recycledmaterialsgarments\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "recycledmaterialsgarments", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table stg.refunds --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.refunds", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ods_vendors_daily SELECT event_name, model_name FROM bus_fare_collection WHERE event_name > 168\"\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": ["event_name", "model_name"]}], "writes": [{"table": "ods_vendors_daily", "columns": ["event_name", "model_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model climate_adaptation_re depends on counties\ndbt build --models climate_adaptation_re --vars 'source: counties'\n", "labels": {"reads": [{"table": "counties", "columns": null}], "writes": [{"table": "climate_adaptation_re", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"restorative_justice_3\")\nsink_to_store(df, \"economic_diversification_argentina\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "restorative_justice_3", "columns": null}], "writes": [{"table": "economic_diversification_argentina", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi.bi_sessions_daily SELECT * FROM legacy\ncur.execute(\"SELECT restypedescription, purchaseid FROM bank LIMIT 384\")\n", "labels": {"reads": [{"table": "bank", "columns": ["restypedescription", "purchaseid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.volunteer_year > 151).all()\n# src table: safety_incidents\nengine.execute(\"INSERT INTO intelligencesatellites SELECT * FROM safety_incidents\")\n", "labels": {"reads": [{"table": "safety_incidents", "columns": null}], "writes": [{"table": "intelligencesatellites", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"job_postings\")\npersist_to_output(df, \"bikerental\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "job_postings", "columns": null}], "writes": [{"table": "bikerental", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ma_inspections --columns users_engaged,farmid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ma_inspections", "columns": ["users_engaged", "farmid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO crops SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_contact_to, attendance FROM aquatic_farms\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"sports\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": ["date_contact_to", "attendance"]}], "writes": [{"table": "sports", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"city_waste_generation\");\ndf.write().mode(\"overwrite\").saveAsTable(\"threatintelligence\");\n", "labels": {"reads": [{"table": "city_waste_generation", "columns": null}], "writes": [{"table": "threatintelligence", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO housing_investments SELECT individual_name, classid, airport_name FROM beauty_products WHERE individual_name > 460\")\n", "labels": {"reads": [{"table": "beauty_products", "columns": ["individual_name", "classid", "airport_name"]}], "writes": [{"table": "housing_investments", "columns": ["individual_name", "classid", "airport_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"travel_advisory\");\ndf.write().mode(\"overwrite\").saveAsTable(\"gameplatforms\");\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": [{"table": "gameplatforms", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"support\").where(\"dt = current_date()\").writeTo(\"school_details\").append()\n", "labels": {"reads": [{"table": "support", "columns": null}], "writes": [{"table": "school_details", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO bi.bi_sessions_df SELECT address_type_code, exoplanet, port_code FROM reservations WHERE address_type_code > 155\")\n", "labels": {"reads": [{"table": "reservations", "columns": ["address_type_code", "exoplanet", "port_code"]}], "writes": [{"table": "bi.bi_sessions_df", "columns": ["address_type_code", "exoplanet", "port_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO waterusage SELECT * FROM legacy\ncur.execute(\"SELECT workshop_group_id, stayid FROM shoes LIMIT 304\")\n", "labels": {"reads": [{"table": "shoes", "columns": ["workshop_group_id", "stayid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO taj_mahal_visitors SELECT stuid, status_of_thing_code FROM evidence_based_policies WHERE stuid > 168\")\n", "labels": {"reads": [{"table": "evidence_based_policies", "columns": ["stuid", "status_of_thing_code"]}], "writes": [{"table": "taj_mahal_visitors", "columns": ["stuid", "status_of_thing_code"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"researchgrants\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "researchgrants", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dw_vendors_di\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dw_vendors_di", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dorm_amenity SELECT a.underrepresented_community, b.involved_in_lifelong_learning FROM models_safety a JOIN train b ON a.cust_id = b.cust_id\"\n", "labels": {"reads": [{"table": "models_safety", "columns": null}, {"table": "train", "columns": null}], "writes": [{"table": "dorm_amenity", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO southeast_providers SELECT * FROM legacy\nspark.sql(\"INSERT INTO locations SELECT continent_id, campaign FROM nz_tourism WHERE continent_id > 425\")\n", "labels": {"reads": [{"table": "nz_tourism", "columns": ["continent_id", "campaign"]}], "writes": [{"table": "locations", "columns": ["continent_id", "campaign"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO characteristics SELECT attendeename, sustainablepractices, hotel_name, yield FROM impact_investments WHERE attendeename > 171\");\n", "labels": {"reads": [{"table": "impact_investments", "columns": ["attendeename", "sustainablepractices", "hotel_name", "yield"]}], "writes": [{"table": "characteristics", "columns": ["attendeename", "sustainablepractices", "hotel_name", "yield"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"coralreefs\")\nsrc.write.insertInto(\"open_pedagogy_courses\", overwrite=True)\n", "labels": {"reads": [{"table": "coralreefs", "columns": null}], "writes": [{"table": "open_pedagogy_courses", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"video_games\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_exposure_di\")\n", "labels": {"reads": [{"table": "video_games", "columns": null}], "writes": [{"table": "ads.ads_exposure_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO customer_payments SELECT price_in_dollar, price FROM carbon_prices_3 WHERE price_in_dollar > 105\"\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": ["price_in_dollar", "price"]}], "writes": [{"table": "customer_payments", "columns": ["price_in_dollar", "price"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table housing_investments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "housing_investments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO conditions SELECT daily_sales, delivery_time, currency_code, characteristic_id FROM mental_health_professionals_2 WHERE daily_sales > 264\"\n", "labels": {"reads": [{"table": "mental_health_professionals_2", "columns": ["daily_sales", "delivery_time", "currency_code", "characteristic_id"]}], "writes": [{"table": "conditions", "columns": ["daily_sales", "delivery_time", "currency_code", "characteristic_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 367;\nEOF\n", "labels": {"reads": [{"table": "nba_games", "columns": ["all_home", "location_text"]}], "writes": [{"table": "mart_exposure_hourly", "columns": ["all_home", "location_text"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"cultivators\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "cultivators", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM benefits_overpayments\", conn)\ndf.to_sql(\"fish_stock\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": null}], "writes": [{"table": "fish_stock", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 46;\nSQL\n", "labels": {"reads": [{"table": "dws.dws_events_df", "columns": ["resource_id", "sessiondate"]}, {"table": "agency_satellites", "columns": ["building_id", "satellite_name", "type"]}], "writes": [{"table": "auto_show", "columns": ["building_id", "satellite_name", "type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vol_id, clinic_name FROM shipment\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"areas\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "shipment", "columns": ["vol_id", "clinic_name"]}], "writes": [{"table": "areas", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO ocean_health_monitor SELECT billing, creator FROM files WHERE billing > 113\")\n", "labels": {"reads": [{"table": "files", "columns": ["billing", "creator"]}], "writes": [{"table": "ocean_health_monitor", "columns": ["billing", "creator"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"competition\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"movie_ratings\")\n", "labels": {"reads": [{"table": "competition", "columns": null}], "writes": [{"table": "movie_ratings", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO smartcityprojects SELECT investmentid, media_type_id FROM host WHERE investmentid > 159\")\n", "labels": {"reads": [{"table": "host", "columns": ["investmentid", "media_type_id"]}], "writes": [{"table": "smartcityprojects", "columns": ["investmentid", "media_type_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO mart.mart_device_log SELECT num_fans, date_formed, vulnerability_score FROM innovation_metrics WHERE num_fans > 259\");\n", "labels": {"reads": [{"table": "innovation_metrics", "columns": ["num_fans", "date_formed", "vulnerability_score"]}], "writes": [{"table": "mart.mart_device_log", "columns": ["num_fans", "date_formed", "vulnerability_score"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO solar_energy SELECT room_number, trench_id FROM salary WHERE room_number > 143\")\n", "labels": {"reads": [{"table": "salary", "columns": ["room_number", "trench_id"]}], "writes": [{"table": "solar_energy", "columns": ["room_number", "trench_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd.dwd_users_hourly\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dwd.dwd_users_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO crops (enrollment, delivery_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "crops", "columns": ["enrollment", "delivery_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO government.city SELECT publicationid, complaint_status_code FROM apartment_buildings WHERE publicationid > 255\");\n", "labels": {"reads": [{"table": "apartment_buildings", "columns": ["publicationid", "complaint_status_code"]}], "writes": [{"table": "government.city", "columns": ["publicationid", "complaint_status_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 365;\nEOF\n", "labels": {"reads": [{"table": "bikerental", "columns": ["participant_id", "wins"]}], "writes": [{"table": "surveylocations", "columns": ["participant_id", "wins"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dw.dw_member_point_hourly\").where(\"dt = current_date()\").writeTo(\"gamedesigndata\").append()\n", "labels": {"reads": [{"table": "dw.dw_member_point_hourly", "columns": null}], "writes": [{"table": "gamedesigndata", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ads.ads_cart_item_hourly --columns artwork,success --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": ["artwork", "success"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO pollutionincidents SELECT a.emp_jobcode, b.restaurant_name FROM invoice_lines a JOIN certifications b ON a.funding_source_type = b.funding_source_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "invoice_lines", "columns": null}, {"table": "certifications", "columns": null}], "writes": [{"table": "pollutionincidents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table movies --target-dir /tmp/land\n", "labels": {"reads": [{"table": "movies", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO accounts SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table peacekeeping_units --columns operationdate,fname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "peacekeeping_units", "columns": ["operationdate", "fname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rehab_centers\").toPandas()\ndf[[\"signupdate\", \"report\"]].to_sql(\"disabilitysupportprograms\", engine, index=False)\n", "labels": {"reads": [{"table": "rehab_centers", "columns": null}], "writes": [{"table": "disabilitysupportprograms", "columns": ["signupdate", "report"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM surveylocations\"\n", "labels": {"reads": [{"table": "surveylocations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO mart_campaigns_delta SELECT location_text, min_age FROM well_production WHERE location_text > 15\")\n", "labels": {"reads": [{"table": "well_production", "columns": ["location_text", "min_age"]}], "writes": [{"table": "mart_campaigns_delta", "columns": ["location_text", "min_age"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO space_debris SELECT date_of_ceremony, production_cost, project_id FROM platformstats WHERE date_of_ceremony > 339\")\n", "labels": {"reads": [{"table": "platformstats", "columns": ["date_of_ceremony", "production_cost", "project_id"]}], "writes": [{"table": "space_debris", "columns": ["date_of_ceremony", "production_cost", "project_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_payments_delta\").toPandas()\ndf[[\"departmentid\", \"missions\"]].to_sql(\"fare_segments\", engine, index=False)\n", "labels": {"reads": [{"table": "ads.ads_payments_delta", "columns": null}], "writes": [{"table": "fare_segments", "columns": ["departmentid", "missions"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"climate_adaptation_re\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "climate_adaptation_re", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO aid_missions SELECT * FROM legacy\ncur.execute(\"SELECT preferred_foot, well_type FROM ods.products_hourly LIMIT 424\")\n", "labels": {"reads": [{"table": "ods.products_hourly", "columns": ["preferred_foot", "well_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO iron_ore_production SELECT * FROM legacy\nspark.sql(\"INSERT INTO mart.mart_risk_score_hourly SELECT played, school_name, cases_handled FROM refugees WHERE played > 489\")\n", "labels": {"reads": [{"table": "refugees", "columns": ["played", "school_name", "cases_handled"]}], "writes": [{"table": "mart.mart_risk_score_hourly", "columns": ["played", "school_name", "cases_handled"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO exhibition_visitors SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO circular_economy_companies SELECT ingredient_name, county_name FROM smartcitytech WHERE ingredient_name > 331\")\n", "labels": {"reads": [{"table": "smartcitytech", "columns": ["ingredient_name", "county_name"]}], "writes": [{"table": "circular_economy_companies", "columns": ["ingredient_name", "county_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO agriculturalinnovations SELECT a.productid, b.case_outcome FROM therapy_session a JOIN environmentalimpact b ON a.iata = b.iata\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "therapy_session", "columns": null}, {"table": "environmentalimpact", "columns": null}], "writes": [{"table": "agriculturalinnovations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO dates SELECT a.local, b.recordid FROM galleries a JOIN companies b ON a.lastdonationdate = b.lastdonationdate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "galleries", "columns": null}, {"table": "companies", "columns": null}], "writes": [{"table": "dates", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ingredient SELECT * FROM legacy\nspark.sql(\"INSERT INTO ads.ads_payments SELECT host_country, spacecraft, instid FROM support_groups WHERE host_country > 130\")\n", "labels": {"reads": [{"table": "support_groups", "columns": ["host_country", "spacecraft", "instid"]}], "writes": [{"table": "ads.ads_payments", "columns": ["host_country", "spacecraft", "instid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"gamesessions\");\ndf.write().mode(\"overwrite\").saveAsTable(\"media_types\");\n", "labels": {"reads": [{"table": "gamesessions", "columns": null}], "writes": [{"table": "media_types", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"inclusive_housing\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"excavationsites\")\n", "labels": {"reads": [{"table": "inclusive_housing", "columns": null}], "writes": [{"table": "excavationsites", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"materials\").where(\"dt = current_date()\").writeTo(\"project_timelines\").append()\n", "labels": {"reads": [{"table": "materials", "columns": null}], "writes": [{"table": "project_timelines", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO innovation_metrics SELECT a.policy_number, b.cause_name FROM trafficviolations a JOIN evsales b ON a.show_name = b.show_name\"\n", "labels": {"reads": [{"table": "trafficviolations", "columns": null}, {"table": "evsales", "columns": null}], "writes": [{"table": "innovation_metrics", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO recycling_centers SELECT * FROM legacy\nspark.sql(\"INSERT INTO submersible_dives SELECT customername, outcome, affiliation FROM dws.inventory_df WHERE customername > 46\")\n", "labels": {"reads": [{"table": "dws.inventory_df", "columns": ["customername", "outcome", "affiliation"]}], "writes": [{"table": "submersible_dives", "columns": ["customername", "outcome", "affiliation"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO leed_buildings (area_id, maintenance_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "leed_buildings", "columns": ["area_id", "maintenance_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"deep_sea_expeditions\")\npush_to_warehouse(df, \"royal_family\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "deep_sea_expeditions", "columns": null}], "writes": [{"table": "royal_family", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO engineer_skills SELECT missiontype, shipping_mode, virtual_tour_views, sex FROM viewership WHERE missiontype > 253\"\n", "labels": {"reads": [{"table": "viewership", "columns": ["missiontype", "shipping_mode", "virtual_tour_views", "sex"]}], "writes": [{"table": "engineer_skills", "columns": ["missiontype", "shipping_mode", "virtual_tour_views", "sex"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table country_landfill_capacity --columns access_count,has_spf --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "country_landfill_capacity", "columns": ["access_count", "has_spf"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"member_data\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "member_data", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO carbon_prices_3 SELECT personnel, garmentid, retweets FROM art_collection WHERE personnel > 50\")\n", "labels": {"reads": [{"table": "art_collection", "columns": ["personnel", "garmentid", "retweets"]}], "writes": [{"table": "carbon_prices_3", "columns": ["personnel", "garmentid", "retweets"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO biodiversity SELECT sustainability_initiative_id, away_team_id, onscholarship, num_fans FROM tech_accessibility_funding WHERE sustainability_initiative_id > 99\")\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": ["sustainability_initiative_id", "away_team_id", "onscholarship", "num_fans"]}], "writes": [{"table": "biodiversity", "columns": ["sustainability_initiative_id", "away_team_id", "onscholarship", "num_fans"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.card_type_code > 244).all()\n# src table: operation\nengine.execute(\"INSERT INTO carbon_offset_programs SELECT * FROM operation\")\n", "labels": {"reads": [{"table": "operation", "columns": null}], "writes": [{"table": "carbon_offset_programs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM circular_economy_initiatives\", conn)\ndf.to_sql(\"impact_asia\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "circular_economy_initiatives", "columns": null}], "writes": [{"table": "impact_asia", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO program SELECT a.resource, b.field_id FROM influencers a JOIN defense_projects_sales b ON a.courtid = b.courtid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "influencers", "columns": null}, {"table": "defense_projects_sales", "columns": null}], "writes": [{"table": "program", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO journal_committee SELECT society, songid, recycler_id FROM seasonalvegetables WHERE society > 420\")\n", "labels": {"reads": [{"table": "seasonalvegetables", "columns": ["society", "songid", "recycler_id"]}], "writes": [{"table": "journal_committee", "columns": ["society", "songid", "recycler_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT start_station_name, circularsupplychain FROM co2_emissions\", engine)\nimport logging\ndf.to_sql(\"trenches\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "co2_emissions", "columns": ["start_station_name", "circularsupplychain"]}], "writes": [{"table": "trenches", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO electricvehicles (task, fabricid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "electricvehicles", "columns": ["task", "fabricid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bike_stations SELECT a.fabrictype, b.tour_type FROM wearable_metrics a JOIN stg.stg_campaigns b ON a.incident_date = b.incident_date\"\n", "labels": {"reads": [{"table": "wearable_metrics", "columns": null}, {"table": "stg.stg_campaigns", "columns": null}], "writes": [{"table": "bike_stations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM safety_violations\"\n", "labels": {"reads": [{"table": "safety_violations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 338;\nEOF\n", "labels": {"reads": [{"table": "auto_shows", "columns": ["disaster_type", "club_name", "document_structure_code"]}], "writes": [{"table": "wind_energy_projects", "columns": ["disaster_type", "club_name", "document_structure_code"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contract_type, founder_ethnicity FROM animal_populations LIMIT 181\")\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO counties SELECT graphics_mode, financially_capable, loadingstart, contactid FROM carbon_emissions WHERE graphics_mode > 199\")\n", "labels": {"reads": [{"table": "animal_populations", "columns": ["contract_type", "founder_ethnicity"]}, {"table": "carbon_emissions", "columns": ["graphics_mode", "financially_capable", "loadingstart", "contactid"]}], "writes": [{"table": "counties", "columns": ["graphics_mode", "financially_capable", "loadingstart", "contactid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO forest SELECT cultivatorid, service_type_description, mental_health_status, policy_type FROM cargo_equipment WHERE cultivatorid > 468\")\n", "labels": {"reads": [{"table": "cargo_equipment", "columns": ["cultivatorid", "service_type_description", "mental_health_status", "policy_type"]}], "writes": [{"table": "forest", "columns": ["cultivatorid", "service_type_description", "mental_health_status", "policy_type"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"volunteer_registration\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 401;\nEOF\n", "labels": {"reads": [{"table": "investments", "columns": ["max_depth", "popularity", "owner_id", "amount_waste"]}], "writes": [{"table": "news_reporting", "columns": ["max_depth", "popularity", "owner_id", "amount_waste"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dw.dw_products_delta SELECT a.organizationname, b.severity FROM region a JOIN event_attendance b ON a.hire_date = b.hire_date\"\n", "labels": {"reads": [{"table": "region", "columns": null}, {"table": "event_attendance", "columns": null}], "writes": [{"table": "dw.dw_products_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"faculty\")\nsrc.write.insertInto(\"bustrips\", overwrite=True)\n", "labels": {"reads": [{"table": "faculty", "columns": null}], "writes": [{"table": "bustrips", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO euro_champs_track_field SELECT siteid, highscore FROM stg.coupon_use_delta WHERE siteid > 296\"\n", "labels": {"reads": [{"table": "stg.coupon_use_delta", "columns": ["siteid", "highscore"]}], "writes": [{"table": "euro_champs_track_field", "columns": ["siteid", "highscore"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO broadband_revenue SELECT amount_payment, participant, movie, vendor_name FROM tracklists WHERE amount_payment > 60\")\n", "labels": {"reads": [{"table": "tracklists", "columns": ["amount_payment", "participant", "movie", "vendor_name"]}], "writes": [{"table": "broadband_revenue", "columns": ["amount_payment", "participant", "movie", "vendor_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"licenses\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "licenses", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.dws_risk_score_df (employee_name, date_and_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_risk_score_df", "columns": ["employee_name", "date_and_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ads.orders SELECT * FROM legacy\nspark.sql(\"INSERT INTO ecohousing SELECT committee, menuname, donation_date FROM inventory WHERE committee > 34\")\n", "labels": {"reads": [{"table": "inventory", "columns": ["committee", "menuname", "donation_date"]}], "writes": [{"table": "ecohousing", "columns": ["committee", "menuname", "donation_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dw_vendors_di --columns age,game --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dw_vendors_di", "columns": ["age", "game"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"follows\")\nsave_to_target(df, \"campaigns_2023\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "follows", "columns": null}], "writes": [{"table": "campaigns_2023", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO deep_sea_species SELECT a.mean_sea_level_pressure_inches, b.account_details FROM school_districts a JOIN aircraft b ON a.domestic_passengers = b.domestic_passengers\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "school_districts", "columns": null}, {"table": "aircraft", "columns": null}], "writes": [{"table": "deep_sea_species", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT customer_phone, founders FROM order_items LIMIT 7\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "order_items", "columns": ["customer_phone", "founders"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.completion_status > 20).all()\n# src table: heritage_sites_3\nengine.execute(\"INSERT INTO ocean_acidification_antarctic SELECT * FROM heritage_sites_3\")\n", "labels": {"reads": [{"table": "heritage_sites_3", "columns": null}], "writes": [{"table": "ocean_acidification_antarctic", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table affiliated_with --target-dir /tmp/land\n", "labels": {"reads": [{"table": "affiliated_with", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO mobile_plans SELECT replacement_cost, rainfall, added_date FROM waste_production WHERE replacement_cost > 5\")\n", "labels": {"reads": [{"table": "waste_production", "columns": ["replacement_cost", "rainfall", "added_date"]}], "writes": [{"table": "mobile_plans", "columns": ["replacement_cost", "rainfall", "added_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ref_calendar --columns student_capacity,hotel_chain_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ref_calendar", "columns": ["student_capacity", "hotel_chain_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sanctuaryanimals\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"infrastructureprojects\")\n", "labels": {"reads": [{"table": "sanctuaryanimals", "columns": null}], "writes": [{"table": "infrastructureprojects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.baseprice > 215).all()\n# src table: chemical\nengine.execute(\"INSERT INTO reporters SELECT * FROM chemical\")\n", "labels": {"reads": [{"table": "chemical", "columns": null}], "writes": [{"table": "reporters", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"underwater_cables\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"haircare_sales\")\n", "labels": {"reads": [{"table": "underwater_cables", "columns": null}], "writes": [{"table": "haircare_sales", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO carbonoffsetinitiatives (funding_id, genre_is) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "carbonoffsetinitiatives", "columns": ["funding_id", "genre_is"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"autonomous_testing\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"container\")\n", "labels": {"reads": [{"table": "autonomous_testing", "columns": null}], "writes": [{"table": "container", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO financial_capability_program SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ucl_top10 (policyholder_id, weeks_on_top) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ucl_top10", "columns": ["policyholder_id", "weeks_on_top"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_table(ctx, \"climate_mitigation_projects\")\nexport_to_target(df, \"global_tournament\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "climate_mitigation_projects", "columns": null}], "writes": [{"table": "global_tournament", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mailshot_campaigns SELECT a.crispr_id, b.attribute_id FROM spaceexploration a JOIN stg.device_log_df b ON a.attribute_data_type = b.attribute_data_type\"\n", "labels": {"reads": [{"table": "spaceexploration", "columns": null}, {"table": "stg.device_log_df", "columns": null}], "writes": [{"table": "mailshot_campaigns", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"candidate_assessments\").toPandas()\ndf[[\"salesperson\", \"text\"]].to_sql(\"sustainable_urban_properties_2\", engine, index=False)\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": null}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["salesperson", "text"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO genetic.projects SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO military_aircraft_maintenance SELECT helpful_votes, feedtype FROM voting_data WHERE helpful_votes > 205\")\n", "labels": {"reads": [{"table": "voting_data", "columns": ["helpful_votes", "feedtype"]}], "writes": [{"table": "military_aircraft_maintenance", "columns": ["helpful_votes", "feedtype"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi_refunds_daily SELECT a.wellbeing_score, b.temperature FROM casebilling a JOIN incident b ON a.health_equity_metric_2 = b.health_equity_metric_2\"\n", "labels": {"reads": [{"table": "casebilling", "columns": null}, {"table": "incident", "columns": null}], "writes": [{"table": "bi_refunds_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO user_likes SELECT insurancetype, loan_amount, stuid, request FROM sustainability_initiatives WHERE insurancetype > 164\"\n", "labels": {"reads": [{"table": "sustainability_initiatives", "columns": ["insurancetype", "loan_amount", "stuid", "request"]}], "writes": [{"table": "user_likes", "columns": ["insurancetype", "loan_amount", "stuid", "request"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"infrastructureprojects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"survey_data\").where(\"dt = current_date()\").writeTo(\"fish_biomass\").append()\n", "labels": {"reads": [{"table": "survey_data", "columns": null}], "writes": [{"table": "fish_biomass", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT staff_address_id, duration_ms FROM ads.ads_risk_score_hourly\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"al_jazeera_data\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": ["staff_address_id", "duration_ms"]}], "writes": [{"table": "al_jazeera_data", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"military_technology_projects\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "military_technology_projects", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO donationsbycause SELECT a.copy_number, b.policy FROM arrivals a JOIN dws.dws_risk_score_df b ON a.asset_details = b.asset_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "arrivals", "columns": null}, {"table": "dws.dws_risk_score_df", "columns": null}], "writes": [{"table": "donationsbycause", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM russia_nato_diplomacy\"\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.device_log\", conn)\ndf.to_sql(\"ingredients\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.device_log", "columns": null}], "writes": [{"table": "ingredients", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 24;\nSQL\n", "labels": {"reads": [{"table": "evidence_based_policies", "columns": ["medication", "editor_id"]}, {"table": "workplace_safety", "columns": ["license_id", "facultyid", "artist_name"]}], "writes": [{"table": "ads.ads_products_full", "columns": ["license_id", "facultyid", "artist_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tourism\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"conditions\")\n", "labels": {"reads": [{"table": "tourism", "columns": null}], "writes": [{"table": "conditions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO food_justice_contributors SELECT a.retweets, b.enable_third_party_ads FROM agri_innovations a JOIN dws_coupon_use b ON a.ironquantity = b.ironquantity\"\n", "labels": {"reads": [{"table": "agri_innovations", "columns": null}, {"table": "dws_coupon_use", "columns": null}], "writes": [{"table": "food_justice_contributors", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_risk_score\").toPandas()\ndf[[\"workshop_name\", \"home_team_id\"]].to_sql(\"electricvehicles\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": null}], "writes": [{"table": "electricvehicles", "columns": ["workshop_name", "home_team_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"founder\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"advisor\")\n", "labels": {"reads": [{"table": "founder", "columns": null}], "writes": [{"table": "advisor", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 134;\nSQL\n", "labels": {"reads": [{"table": "college", "columns": ["num_pallets", "event_type"]}, {"table": "online_platform", "columns": ["fabrictype", "volunteername", "total_amount"]}], "writes": [{"table": "genetics.projects", "columns": ["fabrictype", "volunteername", "total_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"artistsdemographics\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "artistsdemographics", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO workshops SELECT species, spacecraft, media_literacy_score FROM vessel_positions WHERE species > 346\")\n", "labels": {"reads": [{"table": "vessel_positions", "columns": ["species", "spacecraft", "media_literacy_score"]}], "writes": [{"table": "workshops", "columns": ["species", "spacecraft", "media_literacy_score"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"policyadvocacyevents\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "policyadvocacyevents", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO road_construction SELECT * FROM legacy\nspark.sql(\"INSERT INTO dw.member_point_daily SELECT mh_id, participant_type_code, marketing_region_descriptrion, profession FROM platform_production WHERE mh_id > 251\")\n", "labels": {"reads": [{"table": "platform_production", "columns": ["mh_id", "participant_type_code", "marketing_region_descriptrion", "profession"]}], "writes": [{"table": "dw.member_point_daily", "columns": ["mh_id", "participant_type_code", "marketing_region_descriptrion", "profession"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO route_fares SELECT song_year, custid FROM mart.mart_shipments_hourly WHERE song_year > 148\"], check=True)\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": ["song_year", "custid"]}], "writes": [{"table": "route_fares", "columns": ["song_year", "custid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO studies SELECT fund_id, release_date FROM station_crime_rates WHERE fund_id > 428\")\n", "labels": {"reads": [{"table": "station_crime_rates", "columns": ["fund_id", "release_date"]}], "writes": [{"table": "studies", "columns": ["fund_id", "release_date"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ods.ods_risk_score_full\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ods.ods_risk_score_full", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"labor_cost\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "labor_cost", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"impact_investments\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "impact_investments", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT water_depth, organization_name FROM stg_payments_hourly LIMIT 88\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "stg_payments_hourly", "columns": ["water_depth", "organization_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO economic_diversification_argentina SELECT shale_play, headquarters, transaction_value FROM family_cases WHERE shale_play > 452\")\n", "labels": {"reads": [{"table": "family_cases", "columns": ["shale_play", "headquarters", "transaction_value"]}], "writes": [{"table": "economic_diversification_argentina", "columns": ["shale_play", "headquarters", "transaction_value"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO rural_infrastructure SELECT neighborhoodid, eliminated_by, image_url, cost FROM recyclednylongarments WHERE neighborhoodid > 318\"\n", "labels": {"reads": [{"table": "recyclednylongarments", "columns": ["neighborhoodid", "eliminated_by", "image_url", "cost"]}], "writes": [{"table": "rural_infrastructure", "columns": ["neighborhoodid", "eliminated_by", "image_url", "cost"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO menu_items SELECT * FROM legacy\ncur.execute(\"SELECT truck_licence_number, stars FROM cargos LIMIT 407\")\n", "labels": {"reads": [{"table": "cargos", "columns": ["truck_licence_number", "stars"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO urbanagricrop SELECT a.claimamount, b.address_content FROM trenches a JOIN autoshows b ON a.forename = b.forename\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "trenches", "columns": null}, {"table": "autoshows", "columns": null}], "writes": [{"table": "urbanagricrop", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"providers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mental_health_clinics\")\n", "labels": {"reads": [{"table": "providers", "columns": null}], "writes": [{"table": "mental_health_clinics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"judges\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "judges", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO mental_health_parity SELECT galleryid, pname, budget_amount FROM tokyo_motor_show WHERE galleryid > 109\")\n", "labels": {"reads": [{"table": "tokyo_motor_show", "columns": ["galleryid", "pname", "budget_amount"]}], "writes": [{"table": "mental_health_parity", "columns": ["galleryid", "pname", "budget_amount"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ca_menu_items SELECT bats, retail_price, document_code, created_date FROM voyages WHERE bats > 62\"\n", "labels": {"reads": [{"table": "voyages", "columns": ["bats", "retail_price", "document_code", "created_date"]}], "writes": [{"table": "ca_menu_items", "columns": ["bats", "retail_price", "document_code", "created_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO user_ad_interactions SELECT * FROM legacy\nspark.sql(\"INSERT INTO player_sessions SELECT launch_date, art_type, launch_company FROM phone_market WHERE launch_date > 12\")\n", "labels": {"reads": [{"table": "phone_market", "columns": ["launch_date", "art_type", "launch_company"]}], "writes": [{"table": "player_sessions", "columns": ["launch_date", "art_type", "launch_company"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO tourism_activities SELECT veteran_id, ram_mib, dockingdate FROM environmentalimpact WHERE veteran_id > 383\")\n", "labels": {"reads": [{"table": "environmentalimpact", "columns": ["veteran_id", "ram_mib", "dockingdate"]}], "writes": [{"table": "tourism_activities", "columns": ["veteran_id", "ram_mib", "dockingdate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO product_catalog SELECT donorid, co_owner_count, unionname, characteristic_type_code FROM atlantic_ocean WHERE donorid > 11\");\n", "labels": {"reads": [{"table": "atlantic_ocean", "columns": ["donorid", "co_owner_count", "unionname", "characteristic_type_code"]}], "writes": [{"table": "product_catalog", "columns": ["donorid", "co_owner_count", "unionname", "characteristic_type_code"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM agroecology_practices\", conn)\ndf.to_sql(\"bay_area_properties\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "agroecology_practices", "columns": null}], "writes": [{"table": "bay_area_properties", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO bi.refunds_daily SELECT policy, success, attorney_id, campaign_name FROM sustainable_urban_properties_2 WHERE policy > 437\")\n", "labels": {"reads": [{"table": "sustainable_urban_properties_2", "columns": ["policy", "success", "attorney_id", "campaign_name"]}], "writes": [{"table": "bi.refunds_daily", "columns": ["policy", "success", "attorney_id", "campaign_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"section\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads.member_point\")\n", "labels": {"reads": [{"table": "section", "columns": null}], "writes": [{"table": "ads.member_point", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads_cart_item_hourly SELECT hotel_name, emissions FROM astronaut_missions WHERE hotel_name > 220\"], check=True)\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": ["hotel_name", "emissions"]}], "writes": [{"table": "ads_cart_item_hourly", "columns": ["hotel_name", "emissions"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"show\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "show", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO campaigns SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO agroecology_practices SELECT * FROM legacy\nspark.sql(\"INSERT INTO bi.bi_vendors_di SELECT bioreactor_id, operationname FROM automation_tech WHERE bioreactor_id > 450\")\n", "labels": {"reads": [{"table": "automation_tech", "columns": ["bioreactor_id", "operationname"]}], "writes": [{"table": "bi.bi_vendors_di", "columns": ["bioreactor_id", "operationname"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO expensive_space_missions SELECT brand_id, day_number, artpieceid FROM stg.stg_risk_score_df WHERE brand_id > 406\");\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": ["brand_id", "day_number", "artpieceid"]}], "writes": [{"table": "expensive_space_missions", "columns": ["brand_id", "day_number", "artpieceid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"provinces\").toPandas()\ndf[[\"account_type\", \"sqft\"]].to_sql(\"artprograms\", engine, index=False)\n", "labels": {"reads": [{"table": "provinces", "columns": null}], "writes": [{"table": "artprograms", "columns": ["account_type", "sqft"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_orders_full SELECT * FROM legacy\ncur.execute(\"SELECT userid, providerid FROM project LIMIT 102\")\n", "labels": {"reads": [{"table": "project", "columns": ["userid", "providerid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO employeepromotions SELECT beds, hearingdate, stu_fname FROM exam_results WHERE beds > 69\"\n", "labels": {"reads": [{"table": "exam_results", "columns": ["beds", "hearingdate", "stu_fname"]}], "writes": [{"table": "employeepromotions", "columns": ["beds", "hearingdate", "stu_fname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO training_programs SELECT order_id, engagement, police_force FROM office_locations WHERE order_id > 419\");\n", "labels": {"reads": [{"table": "office_locations", "columns": ["order_id", "engagement", "police_force"]}], "writes": [{"table": "training_programs", "columns": ["order_id", "engagement", "police_force"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO chemical_composition SELECT velocity, squadron, owner FROM ads.refunds WHERE velocity > 292\"\n", "labels": {"reads": [{"table": "ads.refunds", "columns": ["velocity", "squadron", "owner"]}], "writes": [{"table": "chemical_composition", "columns": ["velocity", "squadron", "owner"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"climate_finance_asia\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "climate_finance_asia", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dwd.dwd_cart_item_di SELECT donationdate, team_id_br, company_name FROM arctic_weather WHERE donationdate > 331\"\n", "labels": {"reads": [{"table": "arctic_weather", "columns": ["donationdate", "team_id_br", "company_name"]}], "writes": [{"table": "dwd.dwd_cart_item_di", "columns": ["donationdate", "team_id_br", "company_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO project SELECT street_address, heartrate, device FROM foodaid WHERE street_address > 424\");\n", "labels": {"reads": [{"table": "foodaid", "columns": ["street_address", "heartrate", "device"]}], "writes": [{"table": "project", "columns": ["street_address", "heartrate", "device"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO maintenancerequests SELECT password, claim_outcome_code, operation_name FROM evidence_based_policies WHERE password > 267\"\n", "labels": {"reads": [{"table": "evidence_based_policies", "columns": ["password", "claim_outcome_code", "operation_name"]}], "writes": [{"table": "maintenancerequests", "columns": ["password", "claim_outcome_code", "operation_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_test_taken, archeologist FROM athletes\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"savings_programs\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "athletes", "columns": ["date_test_taken", "archeologist"]}], "writes": [{"table": "savings_programs", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dws.dws_risk_score_daily\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dws.dws_risk_score_daily", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"indian_ocean_wells\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "indian_ocean_wells", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"broadband_providers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"species_data\")\n", "labels": {"reads": [{"table": "broadband_providers", "columns": null}], "writes": [{"table": "species_data", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"food_justice\").where(\"dt = current_date()\").writeTo(\"state_usage\").append()\n", "labels": {"reads": [{"table": "food_justice", "columns": null}], "writes": [{"table": "state_usage", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ref_calendar SELECT co_owner_count, farmname, quantity, playdate FROM bi.inventory_delta WHERE co_owner_count > 451\"\n", "labels": {"reads": [{"table": "bi.inventory_delta", "columns": ["co_owner_count", "farmname", "quantity", "playdate"]}], "writes": [{"table": "ref_calendar", "columns": ["co_owner_count", "farmname", "quantity", "playdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table movie_financials --columns supply_volume,join_year --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "movie_financials", "columns": ["supply_volume", "join_year"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"carbon_offset_programs\")\npush_to_warehouse(df, \"providers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": null}], "writes": [{"table": "providers", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.skill_id > 182).all()\n# src table: wastegeneration\nengine.execute(\"INSERT INTO category_revenue SELECT * FROM wastegeneration\")\n", "labels": {"reads": [{"table": "wastegeneration", "columns": null}], "writes": [{"table": "category_revenue", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"exhibition_visitors\");\ndf.write().mode(\"overwrite\").saveAsTable(\"product_characteristics\");\n", "labels": {"reads": [{"table": "exhibition_visitors", "columns": null}], "writes": [{"table": "product_characteristics", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dwd.exposure_hourly SELECT to_address, rural, fleet_id FROM team_franchise WHERE to_address > 90\"\n", "labels": {"reads": [{"table": "team_franchise", "columns": ["to_address", "rural", "fleet_id"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": ["to_address", "rural", "fleet_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO winter_olympics SELECT sportname, expertise, cityname FROM consumer_preference WHERE sportname > 452\")\n", "labels": {"reads": [{"table": "consumer_preference", "columns": ["sportname", "expertise", "cityname"]}], "writes": [{"table": "winter_olympics", "columns": ["sportname", "expertise", "cityname"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO customers_policies SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ods.ods_users_di\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 186;\nSQL\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": ["num_of_audience", "color_code"]}, {"table": "ads.events", "columns": ["trip_distance", "centername"]}], "writes": [{"table": "online_platform", "columns": ["trip_distance", "centername"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO aid_missions SELECT invoice_id, local FROM videos WHERE invoice_id > 324\"\n", "labels": {"reads": [{"table": "videos", "columns": ["invoice_id", "local"]}], "writes": [{"table": "aid_missions", "columns": ["invoice_id", "local"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO material SELECT investmentid, crispr_id, accelerator_id FROM concert_sales WHERE investmentid > 24\")\n", "labels": {"reads": [{"table": "concert_sales", "columns": ["investmentid", "crispr_id", "accelerator_id"]}], "writes": [{"table": "material", "columns": ["investmentid", "crispr_id", "accelerator_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dw_payments\"\n", "labels": {"reads": [{"table": "dw_payments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO economic_diversification_projects SELECT * FROM legacy\ncur.execute(\"SELECT habitat_name, outcome_type FROM biosensors.projects LIMIT 414\")\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": ["habitat_name", "outcome_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO hydro_power SELECT founded_year, departure_date, eliminated_by, developer_id FROM solana_transactions WHERE founded_year > 99\"\n", "labels": {"reads": [{"table": "solana_transactions", "columns": ["founded_year", "departure_date", "eliminated_by", "developer_id"]}], "writes": [{"table": "hydro_power", "columns": ["founded_year", "departure_date", "eliminated_by", "developer_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tracklists\").toPandas()\ndf[[\"category_name\", \"totaldonation\"]].to_sql(\"life_expectancy\", engine, index=False)\n", "labels": {"reads": [{"table": "tracklists", "columns": null}], "writes": [{"table": "life_expectancy", "columns": ["category_name", "totaldonation"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.dish_type > 198).all()\n# src table: art_pieces\nengine.execute(\"INSERT INTO volunteers SELECT * FROM art_pieces\")\n", "labels": {"reads": [{"table": "art_pieces", "columns": null}], "writes": [{"table": "volunteers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO training_programs SELECT area_sqkm, visitorid, track_id, date_left_staff FROM aquatic_farms WHERE area_sqkm > 280\")\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": ["area_sqkm", "visitorid", "track_id", "date_left_staff"]}], "writes": [{"table": "training_programs", "columns": ["area_sqkm", "visitorid", "track_id", "date_left_staff"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"producers\")\ndump_to_target(df, \"safetytests\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "producers", "columns": null}], "writes": [{"table": "safetytests", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO review SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_events_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg_users_daily\")\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}], "writes": [{"table": "stg_users_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"hydro_plants\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"transportation_fleet\")\n", "labels": {"reads": [{"table": "hydro_plants", "columns": null}], "writes": [{"table": "transportation_fleet", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO graduates SELECT mailing_date, role_name FROM solana_transactions WHERE mailing_date > 36\")\n", "labels": {"reads": [{"table": "solana_transactions", "columns": ["mailing_date", "role_name"]}], "writes": [{"table": "graduates", "columns": ["mailing_date", "role_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 43;\nSQL\n", "labels": {"reads": [{"table": "tunnels", "columns": ["expertise", "item_name"]}, {"table": "dwd.dwd_member_point_di", "columns": ["state_province", "wellbeing_score", "major", "gender_code"]}], "writes": [{"table": "call_volume", "columns": ["state_province", "wellbeing_score", "major", "gender_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.events_hourly SELECT fate, event_name, practice_id, enrollment_date FROM crop_temperature WHERE fate > 246\"\n", "labels": {"reads": [{"table": "crop_temperature", "columns": ["fate", "event_name", "practice_id", "enrollment_date"]}], "writes": [{"table": "stg.events_hourly", "columns": ["fate", "event_name", "practice_id", "enrollment_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"wellbeing_programs\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"train_lines\")\nsrc.write.insertInto(\"ocean_temperatures\", overwrite=True)\n", "labels": {"reads": [{"table": "train_lines", "columns": null}], "writes": [{"table": "ocean_temperatures", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.attorney > 213).all()\n# src table: clinical_trials\nengine.execute(\"INSERT INTO airport SELECT * FROM clinical_trials\")\n", "labels": {"reads": [{"table": "clinical_trials", "columns": null}], "writes": [{"table": "airport", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table vehicledata --target-dir /tmp/land\n", "labels": {"reads": [{"table": "vehicledata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO subway SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"vaccine_administered\")\npush_to_sink(df, \"mart.mart_member_point_df\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vaccine_administered", "columns": null}], "writes": [{"table": "mart.mart_member_point_df", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ads_sessions_di depends on well_production\ndbt run --models ads_sessions_di --vars '{\"src\":\"well_production\"}'\n", "labels": {"reads": [{"table": "well_production", "columns": null}], "writes": [{"table": "ads_sessions_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO music_streaming SELECT * FROM legacy\nspark.sql(\"INSERT INTO papers SELECT base_id, ethnicity FROM artifacts WHERE base_id > 190\")\n", "labels": {"reads": [{"table": "artifacts", "columns": ["base_id", "ethnicity"]}], "writes": [{"table": "papers", "columns": ["base_id", "ethnicity"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT faculty, injury FROM shoes LIMIT 199\")\nrows = cur.fetchall()\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "shoes", "columns": ["faculty", "injury"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT mar, tree_id FROM sports\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"journal_committee\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "sports", "columns": ["mar", "tree_id"]}], "writes": [{"table": "journal_committee", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO legal_technology_funding SELECT date_left_staff, people_id FROM carbon_prices_3 WHERE date_left_staff > 125\"\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": ["date_left_staff", "people_id"]}], "writes": [{"table": "legal_technology_funding", "columns": ["date_left_staff", "people_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO nailpolishsales SELECT score, request FROM concentrateprices WHERE score > 231\"\n", "labels": {"reads": [{"table": "concentrateprices", "columns": ["score", "request"]}], "writes": [{"table": "nailpolishsales", "columns": ["score", "request"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dependent\"\n", "labels": {"reads": [{"table": "dependent", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table harvest_permits --columns acc_regular_season,shop_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "harvest_permits", "columns": ["acc_regular_season", "shop_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cerium_production\").toPandas()\ndf[[\"famous_title\", \"strainid\"]].to_sql(\"artcontributors\", engine, index=False)\n", "labels": {"reads": [{"table": "cerium_production", "columns": null}], "writes": [{"table": "artcontributors", "columns": ["famous_title", "strainid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model aquaticfarm depends on coowners\ndbt build --select aquaticfarm --vars 'source: coowners'\n", "labels": {"reads": [{"table": "coowners", "columns": null}], "writes": [{"table": "aquaticfarm", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"apartments\").toPandas()\ndf[[\"total_cost\", \"activity\"]].to_sql(\"fish_feed_factories\", engine, index=False)\n", "labels": {"reads": [{"table": "apartments", "columns": null}], "writes": [{"table": "fish_feed_factories", "columns": ["total_cost", "activity"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO chemical_processes SELECT a.member_in_charge_id, b.appelation FROM sustainabilityratings a JOIN time_dim b ON a.wage = b.wage\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": null}, {"table": "time_dim", "columns": null}], "writes": [{"table": "chemical_processes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"factories\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"workers\")\n", "labels": {"reads": [{"table": "factories", "columns": null}], "writes": [{"table": "workers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sent_date, materialtype FROM multimodalhubs LIMIT 27\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": ["sent_date", "materialtype"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO salesperson SELECT portid, gamepreference FROM recycling_rates WHERE portid > 278\")\n", "labels": {"reads": [{"table": "recycling_rates", "columns": ["portid", "gamepreference"]}], "writes": [{"table": "salesperson", "columns": ["portid", "gamepreference"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dws.payments_delta SELECT * FROM legacy\nspark.sql(\"INSERT INTO playergamehistory SELECT furniture_id, emergency_type FROM patient WHERE furniture_id > 93\")\n", "labels": {"reads": [{"table": "patient", "columns": ["furniture_id", "emergency_type"]}], "writes": [{"table": "playergamehistory", "columns": ["furniture_id", "emergency_type"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 311;\nSQL\n", "labels": {"reads": [{"table": "player", "columns": ["stu_lname", "artifact_id"]}, {"table": "atlantic_marine_life", "columns": ["police_force", "omim", "contractor_id"]}], "writes": [{"table": "election", "columns": ["police_force", "omim", "contractor_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"patient_outcomes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"paris_real_estate\")\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": null}], "writes": [{"table": "paris_real_estate", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table ods.ods_payments_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods.ods_payments_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"agricultural_innovations\")\nsrc.write.insertInto(\"art_exhibit_attendance\", overwrite=True)\n", "labels": {"reads": [{"table": "agricultural_innovations", "columns": null}], "writes": [{"table": "art_exhibit_attendance", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.license_id > 69).all()\n# src table: pipelines\nengine.execute(\"INSERT INTO talent_acquisition SELECT * FROM pipelines\")\n", "labels": {"reads": [{"table": "pipelines", "columns": null}], "writes": [{"table": "talent_acquisition", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table program_budget --columns donor_program,advisoryid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "program_budget", "columns": ["donor_program", "advisoryid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"drug_approval\");\ndf.write().mode(\"overwrite\").saveAsTable(\"mailshot_customers\");\n", "labels": {"reads": [{"table": "drug_approval", "columns": null}], "writes": [{"table": "mailshot_customers", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO circular_economy SELECT a.materialtype, b.caloric_content FROM communityhealthworkerscanada a JOIN legalaidrequests b ON a.ironid = b.ironid\"\n", "labels": {"reads": [{"table": "communityhealthworkerscanada", "columns": null}, {"table": "legalaidrequests", "columns": null}], "writes": [{"table": "circular_economy", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"crops_year\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "crops_year", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"producersnewmexico\")\nsrc.write.insertInto(\"ai_safety_incidents\", overwrite=True)\n", "labels": {"reads": [{"table": "producersnewmexico", "columns": null}], "writes": [{"table": "ai_safety_incidents", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO trainmaintenance SELECT a.money_requested, b.taskid FROM skincareproducts a JOIN operations b ON a.request_id = b.request_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "skincareproducts", "columns": null}, {"table": "operations", "columns": null}], "writes": [{"table": "trainmaintenance", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT swimmer_id, driverid FROM bi.clicks_df\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"cosmetics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bi.clicks_df", "columns": ["swimmer_id", "driverid"]}], "writes": [{"table": "cosmetics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"commercialbuildings\").where(\"dt = current_date()\").writeTo(\"support\").append()\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": null}], "writes": [{"table": "support", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO sustainable_urban SELECT * FROM legacy\nspark.sql(\"INSERT INTO veteran_stats SELECT plant_location, audienceid FROM exhibitions WHERE plant_location > 474\")\n", "labels": {"reads": [{"table": "exhibitions", "columns": ["plant_location", "audienceid"]}], "writes": [{"table": "veteran_stats", "columns": ["plant_location", "audienceid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"affiliated_with\")\nsrc.write.insertInto(\"player_f\", overwrite=True)\n", "labels": {"reads": [{"table": "affiliated_with", "columns": null}], "writes": [{"table": "player_f", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO broadband_customers_global (aircraft, complaintid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "broadband_customers_global", "columns": ["aircraft", "complaintid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO feedback SELECT incident, menucategory, socially_responsible, year_opened FROM round WHERE incident > 475\"\n", "labels": {"reads": [{"table": "round", "columns": ["incident", "menucategory", "socially_responsible", "year_opened"]}], "writes": [{"table": "feedback", "columns": ["incident", "menucategory", "socially_responsible", "year_opened"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_input(ctx, \"contractnegotiations\")\nupsert_to_target(df, \"gamegenres\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "contractnegotiations", "columns": null}], "writes": [{"table": "gamegenres", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"investmentsesg\")\nexport_to_target(df, \"judges\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "investmentsesg", "columns": null}], "writes": [{"table": "judges", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"auto_shows\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "auto_shows", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO global_tournament (line_id, water_temp) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "global_tournament", "columns": ["line_id", "water_temp"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO funding_records SELECT * FROM legacy\nspark.sql(\"INSERT INTO fairtradefactories SELECT container_count, emp_id, disability_type, region_code FROM ingredientsvegancrueltyfree WHERE container_count > 234\")\n", "labels": {"reads": [{"table": "ingredientsvegancrueltyfree", "columns": ["container_count", "emp_id", "disability_type", "region_code"]}], "writes": [{"table": "fairtradefactories", "columns": ["container_count", "emp_id", "disability_type", "region_code"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 424;\nEOF\n", "labels": {"reads": [{"table": "tree_species", "columns": ["pages_per_minute_color", "testtype"]}], "writes": [{"table": "bi.bi_orders_hourly", "columns": ["pages_per_minute_color", "testtype"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO eu_data_usage SELECT account_details, engineer_id FROM test_drives WHERE account_details > 109\"\n", "labels": {"reads": [{"table": "test_drives", "columns": ["account_details", "engineer_id"]}], "writes": [{"table": "eu_data_usage", "columns": ["account_details", "engineer_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attraction_name, registered_date FROM bustrips\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"casesbyyear\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bustrips", "columns": ["attraction_name", "registered_date"]}], "writes": [{"table": "casesbyyear", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ref_shipping_agents\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ref_shipping_agents", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cyber_incidents SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO outcomes SELECT graduate, workout_name, workoutid, fault_log_entry_id FROM co2_emission_reduction WHERE graduate > 78\")\n", "labels": {"reads": [{"table": "co2_emission_reduction", "columns": ["graduate", "workout_name", "workoutid", "fault_log_entry_id"]}], "writes": [{"table": "outcomes", "columns": ["graduate", "workout_name", "workoutid", "fault_log_entry_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table tours --columns co2_reduction_tons,characteristic_type_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "tours", "columns": ["co2_reduction_tons", "characteristic_type_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"emergency_categories\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "emergency_categories", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO innovation_grants (product_size, spacecraft_model) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "innovation_grants", "columns": ["product_size", "spacecraft_model"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"traffic\").where(\"dt = current_date()\").writeTo(\"teacher_pd_hours\").append()\n", "labels": {"reads": [{"table": "traffic", "columns": null}], "writes": [{"table": "teacher_pd_hours", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO auto_show SELECT a.trainingtype, b.agegroup FROM graduates a JOIN dws.risk_score_daily b ON a.chip_model = b.chip_model\"\n", "labels": {"reads": [{"table": "graduates", "columns": null}, {"table": "dws.risk_score_daily", "columns": null}], "writes": [{"table": "auto_show", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table wta_serves --columns focus,employeeid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "wta_serves", "columns": ["focus", "employeeid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.refunds_daily SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"government.region\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stations\")\n", "labels": {"reads": [{"table": "government.region", "columns": null}], "writes": [{"table": "stations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO menuitems SELECT agency_id, visit_date FROM mart.clicks WHERE agency_id > 332\"\n", "labels": {"reads": [{"table": "mart.clicks", "columns": ["agency_id", "visit_date"]}], "writes": [{"table": "menuitems", "columns": ["agency_id", "visit_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO community_development_projects SELECT condition, first_donation_date FROM veteran_employment WHERE condition > 90\");\n", "labels": {"reads": [{"table": "veteran_employment", "columns": ["condition", "first_donation_date"]}], "writes": [{"table": "community_development_projects", "columns": ["condition", "first_donation_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 12;\nSQL\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": ["contributorname", "contact_staff_id"]}, {"table": "multimodal_trips", "columns": ["town_city", "drought_id", "host_id"]}], "writes": [{"table": "policyholders", "columns": ["town_city", "drought_id", "host_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table policy_feedback --columns artwork,event_type_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "policy_feedback", "columns": ["artwork", "event_type_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT accommodationtype, form_name FROM pets LIMIT 484\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO contract_timeline SELECT user_category, launch_agency FROM fishcaught WHERE user_category > 16\")\n", "labels": {"reads": [{"table": "pets", "columns": ["accommodationtype", "form_name"]}, {"table": "fishcaught", "columns": ["user_category", "launch_agency"]}], "writes": [{"table": "contract_timeline", "columns": ["user_category", "launch_agency"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table co2emissions --columns line_number,shipped_to --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "co2emissions", "columns": ["line_number", "shipped_to"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO dw.dw_sessions_full SELECT next_maintenance, shipment_year, likes, book_id FROM renewableenergyprojects WHERE next_maintenance > 127\")\n", "labels": {"reads": [{"table": "renewableenergyprojects", "columns": ["next_maintenance", "shipment_year", "likes", "book_id"]}], "writes": [{"table": "dw.dw_sessions_full", "columns": ["next_maintenance", "shipment_year", "likes", "book_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads.payments_di SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO episodes SELECT maxoccupancy, budget_in_billions, development_name FROM mart.mart_users WHERE maxoccupancy > 165\"\n", "labels": {"reads": [{"table": "mart.mart_users", "columns": ["maxoccupancy", "budget_in_billions", "development_name"]}], "writes": [{"table": "episodes", "columns": ["maxoccupancy", "budget_in_billions", "development_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cargo_weight, away_team_three_point FROM public_transport.passenger_count LIMIT 110\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "public_transport.passenger_count", "columns": ["cargo_weight", "away_team_three_point"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dwd.inventory_df SELECT plan_id, cause FROM cargos WHERE plan_id > 235\"\n", "labels": {"reads": [{"table": "cargos", "columns": ["plan_id", "cause"]}], "writes": [{"table": "dwd.inventory_df", "columns": ["plan_id", "cause"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO patient_outcomes (mine_id, average) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "patient_outcomes", "columns": ["mine_id", "average"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO school_districts SELECT mine_name, is_recycled, primary_advisor FROM weights WHERE mine_name > 78\");\n", "labels": {"reads": [{"table": "weights", "columns": ["mine_name", "is_recycled", "primary_advisor"]}], "writes": [{"table": "school_districts", "columns": ["mine_name", "is_recycled", "primary_advisor"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_ethics_policies\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"temperaturehistory\")\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": null}], "writes": [{"table": "temperaturehistory", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"medical_professionals\");\ndf.write().mode(\"overwrite\").saveAsTable(\"wastewater_treatment_plants\");\n", "labels": {"reads": [{"table": "medical_professionals", "columns": null}], "writes": [{"table": "wastewater_treatment_plants", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO healthbudget SELECT outcome_id, investment_date FROM machine WHERE outcome_id > 481\"], check=True)\n", "labels": {"reads": [{"table": "machine", "columns": ["outcome_id", "investment_date"]}], "writes": [{"table": "healthbudget", "columns": ["outcome_id", "investment_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"military_innovation\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"carbon_offset_initiatives\")\n", "labels": {"reads": [{"table": "military_innovation", "columns": null}], "writes": [{"table": "carbon_offset_initiatives", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO energy_storage SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"aquaculture_farms\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"document_structures\")\n", "labels": {"reads": [{"table": "aquaculture_farms", "columns": null}], "writes": [{"table": "document_structures", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"member_of_club\")\ndump_to_store(df, \"league_x\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "member_of_club", "columns": null}], "writes": [{"table": "league_x", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO artists SELECT * FROM legacy\nspark.sql(\"INSERT INTO vessels_2 SELECT retailer_name, publisher, channel_code, attribute_name FROM volunteers WHERE retailer_name > 233\")\n", "labels": {"reads": [{"table": "volunteers", "columns": ["retailer_name", "publisher", "channel_code", "attribute_name"]}], "writes": [{"table": "vessels_2", "columns": ["retailer_name", "publisher", "channel_code", "attribute_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_inventory_full SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"beauty_products\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "beauty_products", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donationhistory\").toPandas()\ndf[[\"innovation\", \"memberid\"]].to_sql(\"dws.payments_delta\", engine, index=False)\n", "labels": {"reads": [{"table": "donationhistory", "columns": null}], "writes": [{"table": "dws.payments_delta", "columns": ["innovation", "memberid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table initiatives --target-dir /tmp/land\n", "labels": {"reads": [{"table": "initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_table(ctx, \"euroavev\")\nsink_to_sink(df, \"smart_grids\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "euroavev", "columns": null}], "writes": [{"table": "smart_grids", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"member_data\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"sectors\")\n", "labels": {"reads": [{"table": "member_data", "columns": null}], "writes": [{"table": "sectors", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO flight_emissions SELECT a.away_team_score, b.label FROM claims_documents a JOIN bi.member_point_full b ON a.thefttype = b.thefttype\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "claims_documents", "columns": null}, {"table": "bi.member_point_full", "columns": null}], "writes": [{"table": "flight_emissions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 260;\nEOF\n", "labels": {"reads": [{"table": "satellites", "columns": ["payment_date", "contributionid"]}], "writes": [{"table": "subway", "columns": ["payment_date", "contributionid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO museums SELECT total_cost, reviewscore, rig_name, speed FROM playerscores WHERE total_cost > 245\"\n", "labels": {"reads": [{"table": "playerscores", "columns": ["total_cost", "reviewscore", "rig_name", "speed"]}], "writes": [{"table": "museums", "columns": ["total_cost", "reviewscore", "rig_name", "speed"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO community_members (exhibition_id, start_speed) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "community_members", "columns": ["exhibition_id", "start_speed"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"emergencies\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"bi_products\")\n", "labels": {"reads": [{"table": "emergencies", "columns": null}], "writes": [{"table": "bi_products", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO diseases SELECT guest_id, is_accessible, gas_fee FROM sourcing WHERE guest_id > 218\")\n", "labels": {"reads": [{"table": "sourcing", "columns": ["guest_id", "is_accessible", "gas_fee"]}], "writes": [{"table": "diseases", "columns": ["guest_id", "is_accessible", "gas_fee"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT employer_organisation_id, mission_name FROM mart.mart_member_point_df LIMIT 248\")\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO co2_sequestration SELECT assessment_date, show_id, dissolved_oxygen, caloric_content FROM vendors WHERE assessment_date > 486\")\n", "labels": {"reads": [{"table": "mart.mart_member_point_df", "columns": ["employer_organisation_id", "mission_name"]}, {"table": "vendors", "columns": ["assessment_date", "show_id", "dissolved_oxygen", "caloric_content"]}], "writes": [{"table": "co2_sequestration", "columns": ["assessment_date", "show_id", "dissolved_oxygen", "caloric_content"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO attorney_billing_rates (player_id, signupdate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "attorney_billing_rates", "columns": ["player_id", "signupdate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"team\")\nupsert_to_target(df, \"militarypersonnel\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "team", "columns": null}], "writes": [{"table": "militarypersonnel", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM countries\"\n", "labels": {"reads": [{"table": "countries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 296;\nSQL\n", "labels": {"reads": [{"table": "genetics.experiments", "columns": ["date_of_ceremony", "document_type_name"]}, {"table": "urban_initiatives", "columns": ["document_type_name", "commission_pct", "passenger_id", "winery"]}], "writes": [{"table": "blockchain_tech", "columns": ["document_type_name", "commission_pct", "passenger_id", "winery"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT reo_type, loadingstart FROM vessel_performance\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"participation\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "vessel_performance", "columns": ["reo_type", "loadingstart"]}], "writes": [{"table": "participation", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd_coupon_use_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"drug_approvals\")\n", "labels": {"reads": [{"table": "dwd_coupon_use_hourly", "columns": null}], "writes": [{"table": "drug_approvals", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.orders SELECT player_id, next_maintenance, profits_in_billion, regionid FROM customer_events WHERE player_id > 426\"\n", "labels": {"reads": [{"table": "customer_events", "columns": ["player_id", "next_maintenance", "profits_in_billion", "regionid"]}], "writes": [{"table": "ads.orders", "columns": ["player_id", "next_maintenance", "profits_in_billion", "regionid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT min_age, province_id FROM timed_locations_of_things\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"creativeais\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "timed_locations_of_things", "columns": ["min_age", "province_id"]}], "writes": [{"table": "creativeais", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dws.device_log_df SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO african_tourism SELECT * FROM legacy\nspark.sql(\"INSERT INTO brand_info SELECT farmname, staff_id, water_temp FROM coowners WHERE farmname > 391\")\n", "labels": {"reads": [{"table": "coowners", "columns": ["farmname", "staff_id", "water_temp"]}], "writes": [{"table": "brand_info", "columns": ["farmname", "staff_id", "water_temp"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model gameplatforms depends on vr_adopters\ndbt run --models gameplatforms --vars '{\"src\":\"vr_adopters\"}'\n", "labels": {"reads": [{"table": "vr_adopters", "columns": null}], "writes": [{"table": "gameplatforms", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table crops_year --columns garment_name,video_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "crops_year", "columns": ["garment_name", "video_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dwd.exposure_hourly SELECT exploited, classroom, formats, system_type FROM dw.dw_events_di WHERE exploited > 424\"\n", "labels": {"reads": [{"table": "dw.dw_events_di", "columns": ["exploited", "classroom", "formats", "system_type"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": ["exploited", "classroom", "formats", "system_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"episodes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"agriculturalinnovations\")\n", "labels": {"reads": [{"table": "episodes", "columns": null}], "writes": [{"table": "agriculturalinnovations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO cybersecurity_vulnerabilities SELECT a.max_depth, b.mission_id FROM ca_menu_items a JOIN e_scooter_trips b ON a.common_name = b.common_name\"\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": null}, {"table": "e_scooter_trips", "columns": null}], "writes": [{"table": "cybersecurity_vulnerabilities", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO bi.clicks_hourly SELECT education_id, stayid, detection_date FROM has_allergy WHERE education_id > 127\");\n", "labels": {"reads": [{"table": "has_allergy", "columns": ["education_id", "stayid", "detection_date"]}], "writes": [{"table": "bi.clicks_hourly", "columns": ["education_id", "stayid", "detection_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT negotiation_date, date_order_placed FROM carbon_offsets\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"fans_merchandise_basketball\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "carbon_offsets", "columns": ["negotiation_date", "date_order_placed"]}], "writes": [{"table": "fans_merchandise_basketball", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table exit_strategies --columns is_autonomous,record_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "exit_strategies", "columns": ["is_autonomous", "record_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"county\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"conservation_initiatives\")\n", "labels": {"reads": [{"table": "county", "columns": null}], "writes": [{"table": "conservation_initiatives", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO manufacturermaterials SELECT host_id, charging_level, publication_date, permitid FROM defense_contracts_v2 WHERE host_id > 497\"\n", "labels": {"reads": [{"table": "defense_contracts_v2", "columns": ["host_id", "charging_level", "publication_date", "permitid"]}], "writes": [{"table": "manufacturermaterials", "columns": ["host_id", "charging_level", "publication_date", "permitid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO patient SELECT individual_middle_name, productionrate, investment, ai_algorithm_id FROM autonomous_research WHERE individual_middle_name > 500\"\n", "labels": {"reads": [{"table": "autonomous_research", "columns": ["individual_middle_name", "productionrate", "investment", "ai_algorithm_id"]}], "writes": [{"table": "patient", "columns": ["individual_middle_name", "productionrate", "investment", "ai_algorithm_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM militarypersonnel\", conn)\ndf.to_sql(\"mart.inventory_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": null}], "writes": [{"table": "mart.inventory_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO organization_contact_individuals SELECT rows, incident_type, client_name FROM pharmasales WHERE rows > 322\")\n", "labels": {"reads": [{"table": "pharmasales", "columns": ["rows", "incident_type", "client_name"]}], "writes": [{"table": "organization_contact_individuals", "columns": ["rows", "incident_type", "client_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.cart_item_di\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ap_budget\")\n", "labels": {"reads": [{"table": "dws.cart_item_di", "columns": null}], "writes": [{"table": "ap_budget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO watertreatmentplants SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"skincare_sales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "skincare_sales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"properties\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ods.ods_users_di\")\n", "labels": {"reads": [{"table": "properties", "columns": null}], "writes": [{"table": "ods.ods_users_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"competition\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "competition", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO genetic.projects SELECT dept_name, last_service, maintenance_contract_id, observation_id FROM employee WHERE dept_name > 286\");\n", "labels": {"reads": [{"table": "employee", "columns": ["dept_name", "last_service", "maintenance_contract_id", "observation_id"]}], "writes": [{"table": "genetic.projects", "columns": ["dept_name", "last_service", "maintenance_contract_id", "observation_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"associatedheritages\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "associatedheritages", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"city_department\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "city_department", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"inclusivehousingpolicies\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"lawyers\")\n", "labels": {"reads": [{"table": "inclusivehousingpolicies", "columns": null}], "writes": [{"table": "lawyers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"arcticwildlifereserve\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "arcticwildlifereserve", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO renewable_power SELECT * FROM legacy\ncur.execute(\"SELECT attendees, galleryid FROM dwd.dwd_campaigns_df LIMIT 50\")\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": ["attendees", "galleryid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"unionmembers\");\ndf.write().mode(\"overwrite\").saveAsTable(\"products_in_events\");\n", "labels": {"reads": [{"table": "unionmembers", "columns": null}], "writes": [{"table": "products_in_events", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO art_pieces SELECT a.provider, b.bank_id FROM stg.orders_daily a JOIN workout_data b ON a.materialid = b.materialid\"\n", "labels": {"reads": [{"table": "stg.orders_daily", "columns": null}, {"table": "workout_data", "columns": null}], "writes": [{"table": "art_pieces", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO erc20_transactions SELECT sales_amount, transaction_id, supplier FROM public.forest_stats WHERE sales_amount > 42\"\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": ["sales_amount", "transaction_id", "supplier"]}], "writes": [{"table": "erc20_transactions", "columns": ["sales_amount", "transaction_id", "supplier"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vessel_types SELECT destruction_authorised_by_employee_id, branch_id FROM team_franchise WHERE destruction_authorised_by_employee_id > 139\"\n", "labels": {"reads": [{"table": "team_franchise", "columns": ["destruction_authorised_by_employee_id", "branch_id"]}], "writes": [{"table": "vessel_types", "columns": ["destruction_authorised_by_employee_id", "branch_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"aid_missions\")\nsrc.write.insertInto(\"donations2022\", overwrite=True)\n", "labels": {"reads": [{"table": "aid_missions", "columns": null}], "writes": [{"table": "donations2022", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO students_lifelong_learning SELECT certified, group_id FROM news_views WHERE certified > 446\"\n", "labels": {"reads": [{"table": "news_views", "columns": ["certified", "group_id"]}], "writes": [{"table": "students_lifelong_learning", "columns": ["certified", "group_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"criminalcases\")\nexport_to_output(df, \"engineer_visits\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "criminalcases", "columns": null}], "writes": [{"table": "engineer_visits", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model life_expectancy depends on peacekeeping_units\ndbt build --models life_expectancy --vars 'source: peacekeeping_units'\n", "labels": {"reads": [{"table": "peacekeeping_units", "columns": null}], "writes": [{"table": "life_expectancy", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donorgender\").toPandas()\ndf[[\"community\", \"status_of_thing_code\"]].to_sql(\"water_distribution\", engine, index=False)\n", "labels": {"reads": [{"table": "donorgender", "columns": null}], "writes": [{"table": "water_distribution", "columns": ["community", "status_of_thing_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"volunteer_hours\").where(\"dt = current_date()\").writeTo(\"healthbudget\").append()\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": null}], "writes": [{"table": "healthbudget", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model products_booked depends on sustainablebrands\ndbt build --select products_booked --vars '{\"src\":\"sustainablebrands\"}'\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": null}], "writes": [{"table": "products_booked", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hashtags, discovery_date FROM coowners LIMIT 178\")\nimport logging\nspark.sql(\"INSERT INTO cinema SELECT password, sanctuary FROM green_building_materials WHERE password > 60\")\n", "labels": {"reads": [{"table": "coowners", "columns": ["hashtags", "discovery_date"]}, {"table": "green_building_materials", "columns": ["password", "sanctuary"]}], "writes": [{"table": "cinema", "columns": ["password", "sanctuary"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table manufacturersustainability --columns department_id,seating --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "manufacturersustainability", "columns": ["department_id", "seating"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO landfill_capacity_north_america SELECT alert_id, membername, accessible FROM furniture WHERE alert_id > 38\")\n", "labels": {"reads": [{"table": "furniture", "columns": ["alert_id", "membername", "accessible"]}], "writes": [{"table": "landfill_capacity_north_america", "columns": ["alert_id", "membername", "accessible"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 88;\nSQL\n", "labels": {"reads": [{"table": "haircare_sales", "columns": ["comments", "subscribe_date"]}, {"table": "workout", "columns": ["programid", "energy_star_rating", "passenger_id"]}], "writes": [{"table": "permian_basin", "columns": ["programid", "energy_star_rating", "passenger_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT trainingtitle, customer_details FROM invoice LIMIT 275\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "invoice", "columns": ["trainingtitle", "customer_details"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT level, software_platform FROM bi.bi_events_daily LIMIT 310\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": ["level", "software_platform"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO bi.bi_campaigns_daily SELECT opening_hours, amount_settled, student_capacity, loadingend FROM medical_facilities_nyc WHERE opening_hours > 471\")\n", "labels": {"reads": [{"table": "medical_facilities_nyc", "columns": ["opening_hours", "amount_settled", "student_capacity", "loadingend"]}], "writes": [{"table": "bi.bi_campaigns_daily", "columns": ["opening_hours", "amount_settled", "student_capacity", "loadingend"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessel_capacity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"incarcerated\")\n", "labels": {"reads": [{"table": "vessel_capacity", "columns": null}], "writes": [{"table": "incarcerated", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO voyages SELECT servicename, maintenance_contract_id, crs_description FROM volume WHERE servicename > 493\");\n", "labels": {"reads": [{"table": "volume", "columns": ["servicename", "maintenance_contract_id", "crs_description"]}], "writes": [{"table": "voyages", "columns": ["servicename", "maintenance_contract_id", "crs_description"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"donation\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "donation", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 160;\nEOF\n", "labels": {"reads": [{"table": "team", "columns": ["topic", "date_contact_to"]}], "writes": [{"table": "workout_data", "columns": ["topic", "date_contact_to"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"student_mental_health\").where(\"dt = current_date()\").writeTo(\"housingaffordability\").append()\n", "labels": {"reads": [{"table": "student_mental_health", "columns": null}], "writes": [{"table": "housingaffordability", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"team\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "team", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO marine_species_status SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO publications (share_in_percent, restorative_justice) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "publications", "columns": ["share_in_percent", "restorative_justice"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO energy_efficiency_projects SELECT concert_id, attendee_id, labor_cost FROM gamesessions WHERE concert_id > 485\")\n", "labels": {"reads": [{"table": "gamesessions", "columns": ["concert_id", "attendee_id", "labor_cost"]}], "writes": [{"table": "energy_efficiency_projects", "columns": ["concert_id", "attendee_id", "labor_cost"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model crime_incidents depends on transaction\ndbt run -s crime_incidents --vars 'source: transaction'\n", "labels": {"reads": [{"table": "transaction", "columns": null}], "writes": [{"table": "crime_incidents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM marine_life_populations\", conn)\ndf.to_sql(\"shops\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": null}], "writes": [{"table": "shops", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.stu_gpa > 185).all()\n# src table: org_volunteer\nengine.execute(\"INSERT INTO atlantic_plate SELECT * FROM org_volunteer\")\n", "labels": {"reads": [{"table": "org_volunteer", "columns": null}], "writes": [{"table": "atlantic_plate", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"educators\")\nsrc.write.insertInto(\"mental_health_professionals_2\", overwrite=True)\n", "labels": {"reads": [{"table": "educators", "columns": null}], "writes": [{"table": "mental_health_professionals_2", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"fairness_scores\").where(\"dt = current_date()\").writeTo(\"iron_ore_production\").append()\n", "labels": {"reads": [{"table": "fairness_scores", "columns": null}], "writes": [{"table": "iron_ore_production", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO org_climate_finance SELECT quality_rank, visit_date, acc_type FROM average WHERE quality_rank > 257\"\n", "labels": {"reads": [{"table": "average", "columns": ["quality_rank", "visit_date", "acc_type"]}], "writes": [{"table": "org_climate_finance", "columns": ["quality_rank", "visit_date", "acc_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"agri_innovations\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "agri_innovations", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.dws_coupon_use_hourly (date_assigned_to, element_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_coupon_use_hourly", "columns": ["date_assigned_to", "element_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table threatintelligence --target-dir /tmp/land\n", "labels": {"reads": [{"table": "threatintelligence", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO performingartsprograms SELECT tourist_id, issue_month, workouttype FROM manager_award WHERE tourist_id > 209\");\n", "labels": {"reads": [{"table": "manager_award", "columns": ["tourist_id", "issue_month", "workouttype"]}], "writes": [{"table": "performingartsprograms", "columns": ["tourist_id", "issue_month", "workouttype"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_input(ctx, \"uniteddefense.equipmentsales\")\nexport_to_store(df, \"claims_processing_stages\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "uniteddefense.equipmentsales", "columns": null}], "writes": [{"table": "claims_processing_stages", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO performances SELECT 1\"\nlogger.info(msg)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO marine_life_data SELECT therapy_type, volunteerid, jul FROM iron_ore_production WHERE therapy_type > 153\")\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": ["therapy_type", "volunteerid", "jul"]}], "writes": [{"table": "marine_life_data", "columns": ["therapy_type", "volunteerid", "jul"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ticket_sales SELECT date, vehicle_name FROM zip_codes WHERE date > 292\"\n", "labels": {"reads": [{"table": "zip_codes", "columns": ["date", "vehicle_name"]}], "writes": [{"table": "ticket_sales", "columns": ["date", "vehicle_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 27;\nSQL\n", "labels": {"reads": [{"table": "highest_scores", "columns": ["market_share", "inclusive_housing_policy"]}, {"table": "unesco_intangible_heritage", "columns": ["acidity_level", "college_id", "time_month"]}], "writes": [{"table": "bi.bi_exposure_hourly", "columns": ["acidity_level", "college_id", "time_month"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cosmetics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"healthcare_budget\")\n", "labels": {"reads": [{"table": "cosmetics", "columns": null}], "writes": [{"table": "healthcare_budget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"winter_olympics\")\npersist_to_sink(df, \"financialwellbeing\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "winter_olympics", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"patents\");\ndf.write().mode(\"overwrite\").saveAsTable(\"economic_diversification\");\n", "labels": {"reads": [{"table": "patents", "columns": null}], "writes": [{"table": "economic_diversification", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_ratings\").toPandas()\ndf[[\"tour_id\", \"decoration_theme\"]].to_sql(\"attribute_definitions\", engine, index=False)\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": null}], "writes": [{"table": "attribute_definitions", "columns": ["tour_id", "decoration_theme"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO investment_strategies SELECT a.itemname, b.apid FROM authenticationlogs a JOIN dws.coupon_use_di b ON a.topic = b.topic\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "authenticationlogs", "columns": null}, {"table": "dws.coupon_use_di", "columns": null}], "writes": [{"table": "investment_strategies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table communityhealthworkers --columns exhibition_id,shippedcost --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "communityhealthworkers", "columns": ["exhibition_id", "shippedcost"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"unionnegotiations\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"bay_area_properties\")\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": null}], "writes": [{"table": "bay_area_properties", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM co2_sequestration\"\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO individual SELECT fish_count, well_type, birth_date, startyear FROM feed WHERE fish_count > 262\"\n", "labels": {"reads": [{"table": "feed", "columns": ["fish_count", "well_type", "birth_date", "startyear"]}], "writes": [{"table": "individual", "columns": ["fish_count", "well_type", "birth_date", "startyear"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"product_sales\");\ndf.write().mode(\"overwrite\").saveAsTable(\"testtypes\");\n", "labels": {"reads": [{"table": "product_sales", "columns": null}], "writes": [{"table": "testtypes", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO vaccinations SELECT * FROM legacy\nspark.sql(\"INSERT INTO reo_production SELECT projectname, billing_country FROM circuits WHERE projectname > 20\")\n", "labels": {"reads": [{"table": "circuits", "columns": ["projectname", "billing_country"]}], "writes": [{"table": "reo_production", "columns": ["projectname", "billing_country"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"communityengagementmetrics\");\ndf.write().mode(\"overwrite\").saveAsTable(\"mart.mart_shipments_hourly\");\n", "labels": {"reads": [{"table": "communityengagementmetrics", "columns": null}], "writes": [{"table": "mart.mart_shipments_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT recipient_id, issues FROM researchpapers\", engine)\nmetrics.append(round(score, 4))\nimport logging\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"school_districts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "researchpapers", "columns": ["recipient_id", "issues"]}], "writes": [{"table": "school_districts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"location\").where(\"dt = current_date()\").writeTo(\"mart.mart_coupon_use_delta\").append()\n", "labels": {"reads": [{"table": "location", "columns": null}], "writes": [{"table": "mart.mart_coupon_use_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model vehicle_registrations depends on artifactanalysis\ndbt run --models vehicle_registrations --vars '{\"src\":\"artifactanalysis\"}'\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": null}], "writes": [{"table": "vehicle_registrations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"reverselogisticstransactions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"teams\")\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": null}], "writes": [{"table": "teams", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO atlantic_ocean_fish SELECT * FROM legacy\ncur.execute(\"SELECT playdate, coal_reserve_remaining FROM constructionlaborstatistics LIMIT 153\")\n", "labels": {"reads": [{"table": "constructionlaborstatistics", "columns": ["playdate", "coal_reserve_remaining"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO circular_economy_companies SELECT a.business_size, b.ll_id FROM hospitallocations a JOIN dws_coupon_use b ON a.number_cities = b.number_cities\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "hospitallocations", "columns": null}, {"table": "dws_coupon_use", "columns": null}], "writes": [{"table": "circular_economy_companies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO recalls SELECT product_category, deliveryid FROM academic_publications WHERE product_category > 238\")\n", "labels": {"reads": [{"table": "academic_publications", "columns": ["product_category", "deliveryid"]}], "writes": [{"table": "recalls", "columns": ["product_category", "deliveryid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO membership_register_branch SELECT certification, dysprosium_prod, stock FROM sites WHERE certification > 196\"\n", "labels": {"reads": [{"table": "sites", "columns": ["certification", "dysprosium_prod", "stock"]}], "writes": [{"table": "membership_register_branch", "columns": ["certification", "dysprosium_prod", "stock"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 245;\nEOF\n", "labels": {"reads": [{"table": "news_views", "columns": ["hourdate", "pages_per_minute_color"]}], "writes": [{"table": "carbon_pricing", "columns": ["hourdate", "pages_per_minute_color"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"daily_transaction_volume\");\ndf.write().mode(\"overwrite\").saveAsTable(\"projects\");\n", "labels": {"reads": [{"table": "daily_transaction_volume", "columns": null}], "writes": [{"table": "projects", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.mart_device_log_delta SELECT pettype, s_id FROM mart.mart_users WHERE pettype > 47\"\n", "labels": {"reads": [{"table": "mart.mart_users", "columns": ["pettype", "s_id"]}], "writes": [{"table": "mart.mart_device_log_delta", "columns": ["pettype", "s_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 265;\nSQL\n", "labels": {"reads": [{"table": "social_impact_bonds", "columns": ["risk_level", "project_name"]}, {"table": "ods.ods_exposure_delta", "columns": ["refugee_name", "capacity_mw"]}], "writes": [{"table": "emerging_markets.digital_assets", "columns": ["refugee_name", "capacity_mw"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO city_properties SELECT delivery_time, show_id, product_name, architect_id FROM stg_orders_hourly WHERE delivery_time > 381\");\n", "labels": {"reads": [{"table": "stg_orders_hourly", "columns": ["delivery_time", "show_id", "product_name", "architect_id"]}], "writes": [{"table": "city_properties", "columns": ["delivery_time", "show_id", "product_name", "architect_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO classicgame SELECT * FROM legacy\ncur.execute(\"SELECT therapy_id, archaeologist_name FROM esa_missions LIMIT 98\")\n", "labels": {"reads": [{"table": "esa_missions", "columns": ["therapy_id", "archaeologist_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT technology, amount_waste FROM food_justice_orgs\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"imagery_archive\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": ["technology", "amount_waste"]}], "writes": [{"table": "imagery_archive", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dw.dw_member_point_hourly SELECT * FROM legacy\ncur.execute(\"SELECT time_second, sensor_reading FROM diseases LIMIT 139\")\n", "labels": {"reads": [{"table": "diseases", "columns": ["time_second", "sensor_reading"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"team_members\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "team_members", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dwd_payments depends on inspectiondata\ndbt build --select dwd_payments --vars 'source: inspectiondata'\n", "labels": {"reads": [{"table": "inspectiondata", "columns": null}], "writes": [{"table": "dwd_payments", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO transport SELECT capacity, co2_emissions FROM socially_responsible_loans WHERE capacity > 282\"\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": ["capacity", "co2_emissions"]}], "writes": [{"table": "transport", "columns": ["capacity", "co2_emissions"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"resilience_infrastructure\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"contract_timeline\")\n", "labels": {"reads": [{"table": "resilience_infrastructure", "columns": null}], "writes": [{"table": "contract_timeline", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM paris_train\", conn)\ndf.to_sql(\"dw.dw_inventory_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "paris_train", "columns": null}], "writes": [{"table": "dw.dw_inventory_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT trial_success_rate, hours_served FROM habitat_preservation LIMIT 230\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "habitat_preservation", "columns": ["trial_success_rate", "hours_served"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemical_production_5\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"exhibition_artworks\")\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": null}], "writes": [{"table": "exhibition_artworks", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO electronics_factories SELECT projecttype, emp_id FROM certifications WHERE projecttype > 319\"\n", "labels": {"reads": [{"table": "certifications", "columns": ["projecttype", "emp_id"]}], "writes": [{"table": "electronics_factories", "columns": ["projecttype", "emp_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT record_id, strategy_id FROM security_incidents LIMIT 444\")\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO union_members SELECT statement_details, programname FROM book WHERE statement_details > 486\")\n", "labels": {"reads": [{"table": "security_incidents", "columns": ["record_id", "strategy_id"]}, {"table": "book", "columns": ["statement_details", "programname"]}], "writes": [{"table": "union_members", "columns": ["statement_details", "programname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table problem_log --target-dir /tmp/land\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"waterconservationbudget\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cosmetics.lipstick_spf_data\")\n", "labels": {"reads": [{"table": "waterconservationbudget", "columns": null}], "writes": [{"table": "cosmetics.lipstick_spf_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO hospital_visits SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO art_exhibit_attendance SELECT a.booking_end_date, b.port_code FROM publication a JOIN city.community_policing b ON a.low_temperature = b.low_temperature\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "publication", "columns": null}, {"table": "city.community_policing", "columns": null}], "writes": [{"table": "art_exhibit_attendance", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.clicks SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"recovery_program\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "recovery_program", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ca_menu_items SELECT * FROM legacy\nspark.sql(\"INSERT INTO org_donation SELECT funding_amount, inventor_name, center_id, financially_capable FROM mars_missions WHERE funding_amount > 115\")\n", "labels": {"reads": [{"table": "mars_missions", "columns": ["funding_amount", "inventor_name", "center_id", "financially_capable"]}], "writes": [{"table": "org_donation", "columns": ["funding_amount", "inventor_name", "center_id", "financially_capable"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workouts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"rural_economy_2\")\n", "labels": {"reads": [{"table": "workouts", "columns": null}], "writes": [{"table": "rural_economy_2", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_practices\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "sustainable_practices", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO restaurant SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cargo_weight, attorney_id FROM threat_intelligence LIMIT 10\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "threat_intelligence", "columns": ["cargo_weight", "attorney_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO economic_diversification_argentina SELECT workoutdate, launch_year, committee, language FROM daily_industrial_water_usage WHERE workoutdate > 246\"\n", "labels": {"reads": [{"table": "daily_industrial_water_usage", "columns": ["workoutdate", "launch_year", "committee", "language"]}], "writes": [{"table": "economic_diversification_argentina", "columns": ["workoutdate", "launch_year", "committee", "language"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT memberid, addressid FROM port\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"salinity_readings\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "port", "columns": ["memberid", "addressid"]}], "writes": [{"table": "salinity_readings", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT participation_date, time_hour FROM news_stories LIMIT 139\")\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO paris_train SELECT away_team_id, vesselname, school_type FROM criminalcases WHERE away_team_id > 391\")\n", "labels": {"reads": [{"table": "news_stories", "columns": ["participation_date", "time_hour"]}, {"table": "criminalcases", "columns": ["away_team_id", "vesselname", "school_type"]}], "writes": [{"table": "paris_train", "columns": ["away_team_id", "vesselname", "school_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_source(ctx, \"mentalhealthprovider\")\nsink_to_store(df, \"investor\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": null}], "writes": [{"table": "investor", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bookings SELECT a.lender_id, b.porphyria FROM drug_approvals a JOIN hotel_revenue b ON a.size_ha = b.size_ha\"\n", "labels": {"reads": [{"table": "drug_approvals", "columns": null}, {"table": "hotel_revenue", "columns": null}], "writes": [{"table": "bookings", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO dwd.dwd_member_point_full SELECT restorative_justice, student_id, metric_id FROM aircraftsquadrons WHERE restorative_justice > 466\")\n", "labels": {"reads": [{"table": "aircraftsquadrons", "columns": ["restorative_justice", "student_id", "metric_id"]}], "writes": [{"table": "dwd.dwd_member_point_full", "columns": ["restorative_justice", "student_id", "metric_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO climate_adaptation_re SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"caseattorneys\")\nsrc.write.insertInto(\"cultural_events\", overwrite=True)\n", "labels": {"reads": [{"table": "caseattorneys", "columns": null}], "writes": [{"table": "cultural_events", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.college > 437).all()\n# src table: military_equipment\nengine.execute(\"INSERT INTO mental_health_clinics SELECT * FROM military_equipment\")\n", "labels": {"reads": [{"table": "military_equipment", "columns": null}], "writes": [{"table": "mental_health_clinics", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"equipmentsales\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"engineer_skills\")\n", "labels": {"reads": [{"table": "equipmentsales", "columns": null}], "writes": [{"table": "engineer_skills", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"invoice\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"province.human_rights_data\")\n", "labels": {"reads": [{"table": "invoice", "columns": null}], "writes": [{"table": "province.human_rights_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"satellite_missions_large\").where(\"dt = current_date()\").writeTo(\"brandrevenue\").append()\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": null}], "writes": [{"table": "brandrevenue", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO investmentsesg SELECT objectnumber, porphyria FROM forests WHERE objectnumber > 61\")\n", "labels": {"reads": [{"table": "forests", "columns": ["objectnumber", "porphyria"]}], "writes": [{"table": "investmentsesg", "columns": ["objectnumber", "porphyria"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO ads.ads_products_hourly SELECT ethnicity, login_name, industry FROM legal_precedents WHERE ethnicity > 21\");\n", "labels": {"reads": [{"table": "legal_precedents", "columns": ["ethnicity", "login_name", "industry"]}], "writes": [{"table": "ads.ads_products_hourly", "columns": ["ethnicity", "login_name", "industry"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_payments_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cotton_source\")\n", "labels": {"reads": [{"table": "mart_payments_df", "columns": null}], "writes": [{"table": "cotton_source", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO eu_data_usage SELECT booked_amount, asset_id, acidification_level, case_type FROM defenseprojects WHERE booked_amount > 53\");\n", "labels": {"reads": [{"table": "defenseprojects", "columns": ["booked_amount", "asset_id", "acidification_level", "case_type"]}], "writes": [{"table": "eu_data_usage", "columns": ["booked_amount", "asset_id", "acidification_level", "case_type"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO stg.stg_risk_score_df SELECT store_name, part_id, campaign FROM stg.device_log_df WHERE store_name > 422\");\n", "labels": {"reads": [{"table": "stg.device_log_df", "columns": ["store_name", "part_id", "campaign"]}], "writes": [{"table": "stg.stg_risk_score_df", "columns": ["store_name", "part_id", "campaign"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table program_funding_2 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "program_funding_2", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"eventparticipation\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "eventparticipation", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO roads SELECT socially_responsible, oct, event_id, water_type FROM caseattorneys WHERE socially_responsible > 426\");\n", "labels": {"reads": [{"table": "caseattorneys", "columns": ["socially_responsible", "oct", "event_id", "water_type"]}], "writes": [{"table": "roads", "columns": ["socially_responsible", "oct", "event_id", "water_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 27;\nSQL\n", "labels": {"reads": [{"table": "mart.mart_vendors", "columns": ["date_in_location_from", "engagement"]}, {"table": "animal_budget", "columns": ["research_id", "checkin"]}], "writes": [{"table": "atlantic_ocean", "columns": ["research_id", "checkin"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sitem\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ads_cart_item_hourly\")\n", "labels": {"reads": [{"table": "sitem", "columns": null}], "writes": [{"table": "ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO community_health_workers SELECT * FROM legacy\nspark.sql(\"INSERT INTO biomes SELECT num_pallets, minister, characteristic_data_type FROM crime_incidents WHERE num_pallets > 78\")\n", "labels": {"reads": [{"table": "crime_incidents", "columns": ["num_pallets", "minister", "characteristic_data_type"]}], "writes": [{"table": "biomes", "columns": ["num_pallets", "minister", "characteristic_data_type"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ytterbium_supply\").where(\"dt = current_date()\").writeTo(\"mars_spacecraft\").append()\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": null}], "writes": [{"table": "mars_spacecraft", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.signupdate > 442).all()\n# src table: tasks\nengine.execute(\"INSERT INTO assessment_notes SELECT * FROM tasks\")\n", "labels": {"reads": [{"table": "tasks", "columns": null}], "writes": [{"table": "assessment_notes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO virtual_tour_offers SELECT committee, claim_number, trip_duration FROM player_sessions WHERE committee > 223\"\n", "labels": {"reads": [{"table": "player_sessions", "columns": ["committee", "claim_number", "trip_duration"]}], "writes": [{"table": "virtual_tour_offers", "columns": ["committee", "claim_number", "trip_duration"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM art_workshops\"\n", "labels": {"reads": [{"table": "art_workshops", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO community_development.transactions SELECT partid, post_id, inspectiondate, case_type FROM causes_insert_2 WHERE partid > 223\");\n", "labels": {"reads": [{"table": "causes_insert_2", "columns": ["partid", "post_id", "inspectiondate", "case_type"]}], "writes": [{"table": "community_development.transactions", "columns": ["partid", "post_id", "inspectiondate", "case_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO mart.shipments_full SELECT chargeable_amount, signupdate, incidents FROM bus_fare_collection WHERE chargeable_amount > 298\");\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": ["chargeable_amount", "signupdate", "incidents"]}], "writes": [{"table": "mart.shipments_full", "columns": ["chargeable_amount", "signupdate", "incidents"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT room_count, date_became_customer FROM artists LIMIT 147\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO water_consumption SELECT attorney_id, vehicle, num_transactions, port FROM youth_fan_participation WHERE attorney_id > 278\")\n", "labels": {"reads": [{"table": "artists", "columns": ["room_count", "date_became_customer"]}, {"table": "youth_fan_participation", "columns": ["attorney_id", "vehicle", "num_transactions", "port"]}], "writes": [{"table": "water_consumption", "columns": ["attorney_id", "vehicle", "num_transactions", "port"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"medicine\").toPandas()\ndf[[\"art_movement\", \"fish_population\"]].to_sql(\"airportdata\", engine, index=False)\n", "labels": {"reads": [{"table": "medicine", "columns": null}], "writes": [{"table": "airportdata", "columns": ["art_movement", "fish_population"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table news_stories --target-dir /tmp/land\n", "labels": {"reads": [{"table": "news_stories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO spacecraft_manufacturers SELECT decor, cname FROM products WHERE decor > 446\");\n", "labels": {"reads": [{"table": "products", "columns": ["decor", "cname"]}], "writes": [{"table": "spacecraft_manufacturers", "columns": ["decor", "cname"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.coupon_use SELECT destination_name, game_id, audienceid FROM dw_payments WHERE destination_name > 205\"\n", "labels": {"reads": [{"table": "dw_payments", "columns": ["destination_name", "game_id", "audienceid"]}], "writes": [{"table": "stg.coupon_use", "columns": ["destination_name", "game_id", "audienceid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table climate_finance_organizations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model mart_sessions_di depends on music_streaming\ndbt build --select mart_sessions_di --vars '{\"src\":\"music_streaming\"}'\n", "labels": {"reads": [{"table": "music_streaming", "columns": null}], "writes": [{"table": "mart_sessions_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO healthcare_access_v2 SELECT organization_name, architect_id, outcome_type FROM invoice WHERE organization_name > 259\");\n", "labels": {"reads": [{"table": "invoice", "columns": ["organization_name", "architect_id", "outcome_type"]}], "writes": [{"table": "healthcare_access_v2", "columns": ["organization_name", "architect_id", "outcome_type"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dw_payments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dw_payments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO maintenance_schedule SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO workforce_development SELECT material_date, tourists, museumname FROM org_donation WHERE material_date > 382\"\n", "labels": {"reads": [{"table": "org_donation", "columns": ["material_date", "tourists", "museumname"]}], "writes": [{"table": "workforce_development", "columns": ["material_date", "tourists", "museumname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"dwd.dwd_orders_di\")\nsave_to_output(df, \"conservation_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.dwd_orders_di", "columns": null}], "writes": [{"table": "conservation_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO matches SELECT src_apid, production_bopd, billid, business_name FROM mars_spacecraft WHERE src_apid > 80\"\n", "labels": {"reads": [{"table": "mars_spacecraft", "columns": ["src_apid", "production_bopd", "billid", "business_name"]}], "writes": [{"table": "matches", "columns": ["src_apid", "production_bopd", "billid", "business_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"animals\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "animals", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"government.region\");\ndf.write().mode(\"overwrite\").saveAsTable(\"crops_year\");\n", "labels": {"reads": [{"table": "government.region", "columns": null}], "writes": [{"table": "crops_year", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO incident SELECT time_day, all_games, archaeologist_name FROM spacecraft WHERE time_day > 304\")\n", "labels": {"reads": [{"table": "spacecraft", "columns": ["time_day", "all_games", "archaeologist_name"]}], "writes": [{"table": "incident", "columns": ["time_day", "all_games", "archaeologist_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.district_name > 30).all()\n# src table: mediators\nengine.execute(\"INSERT INTO market_trends SELECT * FROM mediators\")\n", "labels": {"reads": [{"table": "mediators", "columns": null}], "writes": [{"table": "market_trends", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"explainable_ai\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"danceevents\")\n", "labels": {"reads": [{"table": "explainable_ai", "columns": null}], "writes": [{"table": "danceevents", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"unionmembers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"fleet\")\n", "labels": {"reads": [{"table": "unionmembers", "columns": null}], "writes": [{"table": "fleet", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.products_daily\", conn)\ndf.to_sql(\"freshwaterfinfish\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": null}], "writes": [{"table": "freshwaterfinfish", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"stg.stg_exposure_daily\")\nsrc.write.insertInto(\"student_addresses\", overwrite=True)\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": null}], "writes": [{"table": "student_addresses", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ods_payments_delta depends on takes\ndbt run -s ods_payments_delta --vars '{\"src\":\"takes\"}'\n", "labels": {"reads": [{"table": "takes", "columns": null}], "writes": [{"table": "ods_payments_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO auctions SELECT claimtype, conferencename FROM danceevents WHERE claimtype > 351\"], check=True)\n", "labels": {"reads": [{"table": "danceevents", "columns": ["claimtype", "conferencename"]}], "writes": [{"table": "auctions", "columns": ["claimtype", "conferencename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.cart_item_full (technician_id, trip_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.cart_item_full", "columns": ["technician_id", "trip_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.orders_daily --columns deliverydate,percentage_change --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.orders_daily", "columns": ["deliverydate", "percentage_change"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fare_segments\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"wrestler\")\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": [{"table": "wrestler", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 208;\nSQL\n", "labels": {"reads": [{"table": "fair_wages", "columns": ["reader_id", "hoursperweek"]}, {"table": "waste_production", "columns": ["dispensary_name", "sustainabilityid", "injury_count", "avg_usage"]}], "writes": [{"table": "runs", "columns": ["dispensary_name", "sustainabilityid", "injury_count", "avg_usage"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"militarypatents\");\ndf.write().mode(\"overwrite\").saveAsTable(\"landfill_capacity_city_v2\");\n", "labels": {"reads": [{"table": "militarypatents", "columns": null}], "writes": [{"table": "landfill_capacity_city_v2", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO space_agencies_2 SELECT organizationid, annual_interchanges, is_accessible, thefttypeid FROM locations WHERE organizationid > 361\"\n", "labels": {"reads": [{"table": "locations", "columns": ["organizationid", "annual_interchanges", "is_accessible", "thefttypeid"]}], "writes": [{"table": "space_agencies_2", "columns": ["organizationid", "annual_interchanges", "is_accessible", "thefttypeid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mart_vendors_full\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "mart_vendors_full", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"safetytestingcounts\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"wind_energy\")\n", "labels": {"reads": [{"table": "safetytestingcounts", "columns": null}], "writes": [{"table": "wind_energy", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"city_tech\").where(\"dt = current_date()\").writeTo(\"brands\").append()\n", "labels": {"reads": [{"table": "city_tech", "columns": null}], "writes": [{"table": "brands", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO purchases SELECT a.production_date, b.case_number FROM clinics_sa a JOIN noise_pollution b ON a.song_name = b.song_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "clinics_sa", "columns": null}, {"table": "noise_pollution", "columns": null}], "writes": [{"table": "purchases", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mars_rovers\").where(\"dt = current_date()\").writeTo(\"food_justice_contributors\").append()\n", "labels": {"reads": [{"table": "mars_rovers", "columns": null}], "writes": [{"table": "food_justice_contributors", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"region_stats\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"artworks\")\n", "labels": {"reads": [{"table": "region_stats", "columns": null}], "writes": [{"table": "artworks", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO rare_earth_companies SELECT drill_count, trainingtype, pilot_name, astronaut_name FROM gender WHERE drill_count > 378\");\n", "labels": {"reads": [{"table": "gender", "columns": ["drill_count", "trainingtype", "pilot_name", "astronaut_name"]}], "writes": [{"table": "rare_earth_companies", "columns": ["drill_count", "trainingtype", "pilot_name", "astronaut_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"fair_trade_brands\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "fair_trade_brands", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO educators SELECT a.equipment_id, b.count_id FROM dws.dws_shipments_full a JOIN dwd_sessions_hourly b ON a.recipient_id = b.recipient_id\"\n", "labels": {"reads": [{"table": "dws.dws_shipments_full", "columns": null}, {"table": "dwd_sessions_hourly", "columns": null}], "writes": [{"table": "educators", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"container\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"review\")\n", "labels": {"reads": [{"table": "container", "columns": null}], "writes": [{"table": "review", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bustrips SELECT head_id, emp_id, trips_on_day, stu_gpa FROM us_military_personnel WHERE head_id > 112\"], check=True)\n", "labels": {"reads": [{"table": "us_military_personnel", "columns": ["head_id", "emp_id", "trips_on_day", "stu_gpa"]}], "writes": [{"table": "bustrips", "columns": ["head_id", "emp_id", "trips_on_day", "stu_gpa"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO vrplayers SELECT siteid, lesson_time, aid_name, trackid FROM london.stations WHERE siteid > 400\"\n", "labels": {"reads": [{"table": "london.stations", "columns": ["siteid", "lesson_time", "aid_name", "trackid"]}], "writes": [{"table": "vrplayers", "columns": ["siteid", "lesson_time", "aid_name", "trackid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO defense_spending SELECT metric_id, graphics_mode, streamid FROM organization WHERE metric_id > 362\")\n", "labels": {"reads": [{"table": "organization", "columns": ["metric_id", "graphics_mode", "streamid"]}], "writes": [{"table": "defense_spending", "columns": ["metric_id", "graphics_mode", "streamid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"water_sources\").toPandas()\ndf[[\"highscore\", \"equipment_id\"]].to_sql(\"co_ownership\", engine, index=False)\n", "labels": {"reads": [{"table": "water_sources", "columns": null}], "writes": [{"table": "co_ownership", "columns": ["highscore", "equipment_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 326;\nSQL\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles_japan", "columns": ["eliminated_by", "region"]}, {"table": "shared_scooters", "columns": ["method_id", "reported"]}], "writes": [{"table": "mart.mart_users_delta", "columns": ["method_id", "reported"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 373;\nSQL\n", "labels": {"reads": [{"table": "list", "columns": ["role_code", "indigenous"]}, {"table": "archaeologists", "columns": ["ngo_name", "ota_id"]}], "writes": [{"table": "ods_risk_score_delta", "columns": ["ngo_name", "ota_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.inventory_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi.inventory_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"student_addresses\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"networkdevices\")\n", "labels": {"reads": [{"table": "student_addresses", "columns": null}], "writes": [{"table": "networkdevices", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT show_times_per_day, surname FROM smartcitycosts LIMIT 447\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO communitydevelopment SELECT address, medical_risk FROM animals WHERE address > 236\")\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": ["show_times_per_day", "surname"]}, {"table": "animals", "columns": ["address", "medical_risk"]}], "writes": [{"table": "communitydevelopment", "columns": ["address", "medical_risk"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO accommodations SELECT sex, productid, exhibit_location FROM menu WHERE sex > 244\")\n", "labels": {"reads": [{"table": "menu", "columns": ["sex", "productid", "exhibit_location"]}], "writes": [{"table": "accommodations", "columns": ["sex", "productid", "exhibit_location"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 237;\nSQL\n", "labels": {"reads": [{"table": "canada_cosmetics_preferences", "columns": ["use_date", "advisor"]}, {"table": "support_groups", "columns": ["genderid", "catalog_id", "crs_credit", "restaurant_id"]}], "writes": [{"table": "constructionlaborstatistics", "columns": ["genderid", "catalog_id", "crs_credit", "restaurant_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd_coupon_use_hourly (item_price, environmental_impact_score) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd_coupon_use_hourly", "columns": ["item_price", "environmental_impact_score"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO pilot_record SELECT artist_name, floor_exercise_points, extraction_date FROM login_attempts WHERE artist_name > 214\");\n", "labels": {"reads": [{"table": "login_attempts", "columns": ["artist_name", "floor_exercise_points", "extraction_date"]}], "writes": [{"table": "pilot_record", "columns": ["artist_name", "floor_exercise_points", "extraction_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"public.police_calls\").where(\"dt = current_date()\").writeTo(\"public_participation\").append()\n", "labels": {"reads": [{"table": "public.police_calls", "columns": null}], "writes": [{"table": "public_participation", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"disaster_response\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"submersible_dives\")\n", "labels": {"reads": [{"table": "disaster_response", "columns": null}], "writes": [{"table": "submersible_dives", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO driverstandings SELECT treatment_date, investors FROM specieswatertemp WHERE treatment_date > 195\")\n", "labels": {"reads": [{"table": "specieswatertemp", "columns": ["treatment_date", "investors"]}], "writes": [{"table": "driverstandings", "columns": ["treatment_date", "investors"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"roller_coaster\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"product_info\")\n", "labels": {"reads": [{"table": "roller_coaster", "columns": null}], "writes": [{"table": "product_info", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dw.dw_sessions_full (log_entry_date, theatrename) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_sessions_full", "columns": ["log_entry_date", "theatrename"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table wastewater_treatment_plants --columns thefttype,total_attendance --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "wastewater_treatment_plants", "columns": ["thefttype", "total_attendance"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO carbon_offset_south_america (visit_details, retailer_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "carbon_offset_south_america", "columns": ["visit_details", "retailer_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM grapes\", conn)\ndf.to_sql(\"cosmetic_sales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "grapes", "columns": null}], "writes": [{"table": "cosmetic_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 107;\nEOF\n", "labels": {"reads": [{"table": "athletes", "columns": ["entryid", "fish_population", "image_data"]}], "writes": [{"table": "workout_data", "columns": ["entryid", "fish_population", "image_data"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table fabric --columns project_details,teamid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "fabric", "columns": ["project_details", "teamid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table trainers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "trainers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT home_team_score, build_year FROM job_postings LIMIT 262\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "job_postings", "columns": ["home_team_score", "build_year"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model therapy_attendance depends on clinics_sa\ndbt run --select therapy_attendance --vars 'source: clinics_sa'\n", "labels": {"reads": [{"table": "clinics_sa", "columns": null}], "writes": [{"table": "therapy_attendance", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 212;\nSQL\n", "labels": {"reads": [{"table": "geothermal_power_plants", "columns": ["round_amount", "recordid"]}, {"table": "criminal_justice_reform_initiatives", "columns": ["media_literacy_score", "watertemp", "shipping_agent_code", "business_size"]}], "writes": [{"table": "solar_farms", "columns": ["media_literacy_score", "watertemp", "shipping_agent_code", "business_size"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO emergencyservices SELECT a.amount_of_refund, b.playerregion FROM farms a JOIN smartcityprojects b ON a.vulnerability_score = b.vulnerability_score\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "farms", "columns": null}, {"table": "smartcityprojects", "columns": null}], "writes": [{"table": "emergencyservices", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO carbon_offset_programs (artworkyear, platform_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "carbon_offset_programs", "columns": ["artworkyear", "platform_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO hotel_ratings (threat, vaccinations) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "hotel_ratings", "columns": ["threat", "vaccinations"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO gameplatforms SELECT matchid, creationyear, exit_date FROM cyber_incidents WHERE matchid > 165\")\n", "labels": {"reads": [{"table": "cyber_incidents", "columns": ["matchid", "creationyear", "exit_date"]}], "writes": [{"table": "gameplatforms", "columns": ["matchid", "creationyear", "exit_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"dwd.dwd_orders_di\")\nupsert_to_sink(df, \"production\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.dwd_orders_di", "columns": null}], "writes": [{"table": "production", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO healthcare SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.stg_users_di SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model network_infrastructure depends on dw_users_full\ndbt build -s network_infrastructure --vars '{\"src\":\"dw_users_full\"}'\n", "labels": {"reads": [{"table": "dw_users_full", "columns": null}], "writes": [{"table": "network_infrastructure", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO online_travel_agency SELECT scores, extraction_state, trip_city, coupon_id FROM steps WHERE scores > 387\");\n", "labels": {"reads": [{"table": "steps", "columns": ["scores", "extraction_state", "trip_city", "coupon_id"]}], "writes": [{"table": "online_travel_agency", "columns": ["scores", "extraction_state", "trip_city", "coupon_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT grant_type, birthdate FROM contract_negotiations LIMIT 363\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO dwd.coupon_use_daily SELECT accommodationtype, last_workout_date, volume FROM legalaidrequests WHERE accommodationtype > 208\")\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": ["grant_type", "birthdate"]}, {"table": "legalaidrequests", "columns": ["accommodationtype", "last_workout_date", "volume"]}], "writes": [{"table": "dwd.coupon_use_daily", "columns": ["accommodationtype", "last_workout_date", "volume"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contributorname, archaeologistid FROM freshwater_fish_farms LIMIT 464\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO fossil_fuel_vehicles SELECT investment_name, mid FROM public.crime_types WHERE investment_name > 349\")\n", "labels": {"reads": [{"table": "freshwater_fish_farms", "columns": ["contributorname", "archaeologistid"]}, {"table": "public.crime_types", "columns": ["investment_name", "mid"]}], "writes": [{"table": "fossil_fuel_vehicles", "columns": ["investment_name", "mid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 20;\nSQL\n", "labels": {"reads": [{"table": "open_pedagogy", "columns": ["date_of_latest_logon", "therapy_type"]}, {"table": "consumer", "columns": ["inspectionid", "tripid", "person_name"]}], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": ["inspectionid", "tripid", "person_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO arcticwildlifereserve SELECT a.stu_lname, b.park FROM communityengagement a JOIN ods.shipments_df b ON a.crs_description = b.crs_description\"\n", "labels": {"reads": [{"table": "communityengagement", "columns": null}, {"table": "ods.shipments_df", "columns": null}], "writes": [{"table": "arcticwildlifereserve", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT waste_type, subject_area_id FROM complaints LIMIT 176\")\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO wins SELECT part_name, countryid, task_details, members FROM harvest_permits WHERE part_name > 98\")\n", "labels": {"reads": [{"table": "complaints", "columns": ["waste_type", "subject_area_id"]}, {"table": "harvest_permits", "columns": ["part_name", "countryid", "task_details", "members"]}], "writes": [{"table": "wins", "columns": ["part_name", "countryid", "task_details", "members"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_health_monitor\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ocean_acidification_antarctic\")\n", "labels": {"reads": [{"table": "ocean_health_monitor", "columns": null}], "writes": [{"table": "ocean_acidification_antarctic", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"enzyme\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"genetics.experiments\")\n", "labels": {"reads": [{"table": "enzyme", "columns": null}], "writes": [{"table": "genetics.experiments", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM drills\"\n", "labels": {"reads": [{"table": "drills", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO passenger_trips SELECT a.appointment_date, b.unit_name FROM member_details a JOIN producersnewmexico b ON a.average_attendance = b.average_attendance\"\n", "labels": {"reads": [{"table": "member_details", "columns": null}, {"table": "producersnewmexico", "columns": null}], "writes": [{"table": "passenger_trips", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table financial_capability_program --columns machine_id,mascot --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "financial_capability_program", "columns": ["machine_id", "mascot"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hotel_name, facility_id FROM funding_records LIMIT 437\")\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO coral_reefs SELECT completed, f_id FROM ods.clicks_full WHERE completed > 216\")\n", "labels": {"reads": [{"table": "funding_records", "columns": ["hotel_name", "facility_id"]}, {"table": "ods.clicks_full", "columns": ["completed", "f_id"]}], "writes": [{"table": "coral_reefs", "columns": ["completed", "f_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dailyapplestreams\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"province.human_rights_data\")\n", "labels": {"reads": [{"table": "dailyapplestreams", "columns": null}], "writes": [{"table": "province.human_rights_data", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO device_usage (living_wage, animal) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "device_usage", "columns": ["living_wage", "animal"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO noise_pollution SELECT * FROM legacy\nspark.sql(\"INSERT INTO online_travel_agency SELECT transact_count, amount_payment FROM historicalcontexts WHERE transact_count > 253\")\n", "labels": {"reads": [{"table": "historicalcontexts", "columns": ["transact_count", "amount_payment"]}], "writes": [{"table": "online_travel_agency", "columns": ["transact_count", "amount_payment"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dwd_events_delta SELECT dlocation, billid FROM customer_address_history WHERE dlocation > 122\"], check=True)\n", "labels": {"reads": [{"table": "customer_address_history", "columns": ["dlocation", "billid"]}], "writes": [{"table": "dwd_events_delta", "columns": ["dlocation", "billid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"timed_locations_of_things\").where(\"dt = current_date()\").writeTo(\"stg_orders_hourly\").append()\n", "labels": {"reads": [{"table": "timed_locations_of_things", "columns": null}], "writes": [{"table": "stg_orders_hourly", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"garmentproduction\")\nsrc.write.insertInto(\"nba_games\", overwrite=True)\n", "labels": {"reads": [{"table": "garmentproduction", "columns": null}], "writes": [{"table": "nba_games", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO gene SELECT count_time, dish_name, time_month, service_details FROM donation WHERE count_time > 324\");\n", "labels": {"reads": [{"table": "donation", "columns": ["count_time", "dish_name", "time_month", "service_details"]}], "writes": [{"table": "gene", "columns": ["count_time", "dish_name", "time_month", "service_details"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model soccer_goals depends on size\ndbt run --models soccer_goals --vars '{\"source_table\":\"size\"}'\n", "labels": {"reads": [{"table": "size", "columns": null}], "writes": [{"table": "soccer_goals", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO cosmetics.lipstick_spf_data SELECT product_price, facility_code, engagement_date, customer_email_address FROM organicproducts WHERE product_price > 16\");\n", "labels": {"reads": [{"table": "organicproducts", "columns": ["product_price", "facility_code", "engagement_date", "customer_email_address"]}], "writes": [{"table": "cosmetics.lipstick_spf_data", "columns": ["product_price", "facility_code", "engagement_date", "customer_email_address"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO pollutionincidents SELECT * FROM legacy\nspark.sql(\"INSERT INTO reservations SELECT avg_yield, all_games FROM talent_acquisition WHERE avg_yield > 289\")\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": ["avg_yield", "all_games"]}], "writes": [{"table": "reservations", "columns": ["avg_yield", "all_games"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO cb_agreements SELECT missions, tech_type FROM bay_area_properties WHERE missions > 377\"\n", "labels": {"reads": [{"table": "bay_area_properties", "columns": ["missions", "tech_type"]}], "writes": [{"table": "cb_agreements", "columns": ["missions", "tech_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dws_inventory_di depends on seamounts\ndbt build --models dws_inventory_di --vars '{\"src\":\"seamounts\"}'\n", "labels": {"reads": [{"table": "seamounts", "columns": null}], "writes": [{"table": "dws_inventory_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO inst SELECT menu_id, nurse FROM purchases WHERE menu_id > 181\");\n", "labels": {"reads": [{"table": "purchases", "columns": ["menu_id", "nurse"]}], "writes": [{"table": "inst", "columns": ["menu_id", "nurse"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table restorative_justice_center --columns review_id,characteristic_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "restorative_justice_center", "columns": ["review_id", "characteristic_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads_users_hourly (subject_name, date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads_users_hourly", "columns": ["subject_name", "date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 475;\nSQL\n", "labels": {"reads": [{"table": "specieswatertemp", "columns": ["segment_id", "teamid"]}, {"table": "intelligence_agents", "columns": ["day_of_week", "formats", "sessionid"]}], "writes": [{"table": "whale_sharks", "columns": ["day_of_week", "formats", "sessionid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_table(ctx, \"exit_strategy\")\nexport_to_warehouse(df, \"financial_transactions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "exit_strategy", "columns": null}], "writes": [{"table": "financial_transactions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dysprosiumproduction SELECT * FROM legacy\nspark.sql(\"INSERT INTO dp_articles SELECT ingredient_name, hire_date FROM community_health_centers WHERE ingredient_name > 239\")\n", "labels": {"reads": [{"table": "community_health_centers", "columns": ["ingredient_name", "hire_date"]}], "writes": [{"table": "dp_articles", "columns": ["ingredient_name", "hire_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO satellitematerials SELECT funding_source, building_address FROM safety_records WHERE funding_source > 339\"\n", "labels": {"reads": [{"table": "safety_records", "columns": ["funding_source", "building_address"]}], "writes": [{"table": "satellitematerials", "columns": ["funding_source", "building_address"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO organization_contact_individuals SELECT expeditionid, bus_id, directed_by, product_subcategory FROM underwater_cables WHERE expeditionid > 424\"\n", "labels": {"reads": [{"table": "underwater_cables", "columns": ["expeditionid", "bus_id", "directed_by", "product_subcategory"]}], "writes": [{"table": "organization_contact_individuals", "columns": ["expeditionid", "bus_id", "directed_by", "product_subcategory"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO factory_water SELECT a.is_valid, b.leader_name FROM renewable_projects a JOIN solar_plants b ON a.price_in_dollars = b.price_in_dollars\"\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}, {"table": "solar_plants", "columns": null}], "writes": [{"table": "factory_water", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi.bi_orders_delta\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "bi.bi_orders_delta", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT nid, community_size FROM properties\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"genetics.projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "properties", "columns": ["nid", "community_size"]}], "writes": [{"table": "genetics.projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO org_donation SELECT * FROM legacy\nspark.sql(\"INSERT INTO military_expenditure SELECT hometeam, product_description, warehouse_name, case_id FROM runs WHERE hometeam > 495\")\n", "labels": {"reads": [{"table": "runs", "columns": ["hometeam", "product_description", "warehouse_name", "case_id"]}], "writes": [{"table": "military_expenditure", "columns": ["hometeam", "product_description", "warehouse_name", "case_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO harvest_permits SELECT a.dish_name, b.subject FROM genre_songs a JOIN ads.ads_payments b ON a.plantlocation = b.plantlocation\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "genre_songs", "columns": null}, {"table": "ads.ads_payments", "columns": null}], "writes": [{"table": "harvest_permits", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT developer_id, delivery_status FROM catalog_contents_additional_attributes\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"traffic_citations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "catalog_contents_additional_attributes", "columns": ["developer_id", "delivery_status"]}], "writes": [{"table": "traffic_citations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO territory.human_rights_data (star_rating_code, donor_program) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "territory.human_rights_data", "columns": ["star_rating_code", "donor_program"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM procedures\"\n", "labels": {"reads": [{"table": "procedures", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM labor_costs\", conn)\ndf.to_sql(\"employment\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "labor_costs", "columns": null}], "writes": [{"table": "employment", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"affordablehousing\").toPandas()\ndf[[\"conferenceid\", \"train_id\"]].to_sql(\"waste_management_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "affordablehousing", "columns": null}], "writes": [{"table": "waste_management_projects", "columns": ["conferenceid", "train_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads_orders SELECT a.union_id, b.vendor_state FROM hospital_equipment a JOIN consumer_preference b ON a.dname = b.dname\"\n", "labels": {"reads": [{"table": "hospital_equipment", "columns": null}, {"table": "consumer_preference", "columns": null}], "writes": [{"table": "ads_orders", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"recyclingrates\")\nsrc.write.insertInto(\"bi.coupon_use\", overwrite=True)\n", "labels": {"reads": [{"table": "recyclingrates", "columns": null}], "writes": [{"table": "bi.coupon_use", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 273;\nEOF\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": ["staff_address_id", "culturalcompetency", "awards"]}], "writes": [{"table": "mart.mart_payments_df", "columns": ["staff_address_id", "culturalcompetency", "awards"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"dwd.products_di\")\nsave_to_warehouse(df, \"purchases\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.products_di", "columns": null}], "writes": [{"table": "purchases", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO inclusion_efforts SELECT farmland_id, prepnurse, organisation_id FROM daily_production WHERE farmland_id > 362\"\n", "labels": {"reads": [{"table": "daily_production", "columns": ["farmland_id", "prepnurse", "organisation_id"]}], "writes": [{"table": "inclusion_efforts", "columns": ["farmland_id", "prepnurse", "organisation_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 57;\nEOF\n", "labels": {"reads": [{"table": "film_category", "columns": ["trade_name", "launch_date", "releasedate"]}], "writes": [{"table": "total_consumption", "columns": ["trade_name", "launch_date", "releasedate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dancefunding SELECT * FROM legacy\ncur.execute(\"SELECT fabricid, game_id FROM tweets LIMIT 39\")\n", "labels": {"reads": [{"table": "tweets", "columns": ["fabricid", "game_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO intelligence_agency SELECT menuitem, organization_id, sector FROM employee_demographics WHERE menuitem > 146\");\n", "labels": {"reads": [{"table": "employee_demographics", "columns": ["menuitem", "organization_id", "sector"]}], "writes": [{"table": "intelligence_agency", "columns": ["menuitem", "organization_id", "sector"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO platformh SELECT visitid, mental_health_status FROM disasters WHERE visitid > 295\")\n", "labels": {"reads": [{"table": "disasters", "columns": ["visitid", "mental_health_status"]}], "writes": [{"table": "platformh", "columns": ["visitid", "mental_health_status"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 305;\nEOF\n", "labels": {"reads": [{"table": "community_leaders", "columns": ["fare_id", "founding_year", "productcategory"]}], "writes": [{"table": "agri_innov", "columns": ["fare_id", "founding_year", "productcategory"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table maintenance_schedule --columns union_name,biz_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "maintenance_schedule", "columns": ["union_name", "biz_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"shariah_compliant_finance\")\nsrc.write.insertInto(\"item\", overwrite=True)\n", "labels": {"reads": [{"table": "shariah_compliant_finance", "columns": null}], "writes": [{"table": "item", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"volunteer_events\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"smartcitycosts\")\n", "labels": {"reads": [{"table": "volunteer_events", "columns": null}], "writes": [{"table": "smartcitycosts", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 262;\nSQL\n", "labels": {"reads": [{"table": "economic_diversification_argentina", "columns": ["special_features", "company_name"]}, {"table": "program_funding_2", "columns": ["home_games", "vehicle_id", "development_name"]}], "writes": [{"table": "ref_detention_type", "columns": ["home_games", "vehicle_id", "development_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"thefttypes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"infrastructurebudget\")\n", "labels": {"reads": [{"table": "thefttypes", "columns": null}], "writes": [{"table": "infrastructurebudget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO department_publications SELECT actual_delivery_date, trial_year, outcome_type, jul FROM ecohousing WHERE actual_delivery_date > 66\"\n", "labels": {"reads": [{"table": "ecohousing", "columns": ["actual_delivery_date", "trial_year", "outcome_type", "jul"]}], "writes": [{"table": "department_publications", "columns": ["actual_delivery_date", "trial_year", "outcome_type", "jul"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT festival_name, working_horses FROM field5 LIMIT 184\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "field5", "columns": ["festival_name", "working_horses"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT ship_date, fuelconsumed FROM ingredients LIMIT 253\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO submarine_canyons SELECT bats, college_id FROM satelliteimagery WHERE bats > 467\")\n", "labels": {"reads": [{"table": "ingredients", "columns": ["ship_date", "fuelconsumed"]}, {"table": "satelliteimagery", "columns": ["bats", "college_id"]}], "writes": [{"table": "submarine_canyons", "columns": ["bats", "college_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.device_log_hourly SELECT ram_mib, swimmer_id, debate_id FROM lives_in WHERE ram_mib > 223\"], check=True)\n", "labels": {"reads": [{"table": "lives_in", "columns": ["ram_mib", "swimmer_id", "debate_id"]}], "writes": [{"table": "mart.device_log_hourly", "columns": ["ram_mib", "swimmer_id", "debate_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT closuredate, preference_rating FROM safety_incidents_india\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"branch\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": ["closuredate", "preference_rating"]}], "writes": [{"table": "branch", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM contractnegotiations\", conn)\ndf.to_sql(\"dwd.dwd_member_point_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "contractnegotiations", "columns": null}], "writes": [{"table": "dwd.dwd_member_point_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO employment SELECT a.affected_population, b.menu_item FROM acidification_data a JOIN org_comms b ON a.sqft = b.sqft\"\n", "labels": {"reads": [{"table": "acidification_data", "columns": null}, {"table": "org_comms", "columns": null}], "writes": [{"table": "employment", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"disaster_mitigation\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artifactanalysis\")\n", "labels": {"reads": [{"table": "disaster_mitigation", "columns": null}], "writes": [{"table": "artifactanalysis", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO article_views SELECT destination_id, first_donation_date FROM safetyincidents WHERE destination_id > 106\");\n", "labels": {"reads": [{"table": "safetyincidents", "columns": ["destination_id", "first_donation_date"]}], "writes": [{"table": "article_views", "columns": ["destination_id", "first_donation_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"infrastructure\")\nsrc.write.insertInto(\"ads_payments_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "infrastructure", "columns": null}], "writes": [{"table": "ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO smartcitycosts SELECT adoption_date, lender_id, cargoid FROM energy_prices WHERE adoption_date > 343\"\n", "labels": {"reads": [{"table": "energy_prices", "columns": ["adoption_date", "lender_id", "cargoid"]}], "writes": [{"table": "smartcitycosts", "columns": ["adoption_date", "lender_id", "cargoid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT gamename, reported_by_staff_id FROM states LIMIT 368\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "states", "columns": ["gamename", "reported_by_staff_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ticketsales\"\n", "labels": {"reads": [{"table": "ticketsales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO leo_missions SELECT mean_sea_level_pressure_inches, subscriber_id FROM london.stations WHERE mean_sea_level_pressure_inches > 428\")\n", "labels": {"reads": [{"table": "london.stations", "columns": ["mean_sea_level_pressure_inches", "subscriber_id"]}], "writes": [{"table": "leo_missions", "columns": ["mean_sea_level_pressure_inches", "subscriber_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 247;\nEOF\n", "labels": {"reads": [{"table": "songs", "columns": ["meeting_count", "low_estimate", "percentage_change"]}], "writes": [{"table": "shariah_financing", "columns": ["meeting_count", "low_estimate", "percentage_change"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT launch_year, outcome_name FROM unionnegotiations\", engine)\nimport logging\ndf.to_sql(\"infrastructurebudget\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": ["launch_year", "outcome_name"]}], "writes": [{"table": "infrastructurebudget", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.productcategory > 258).all()\n# src table: communityengagements\nengine.execute(\"INSERT INTO threats SELECT * FROM communityengagements\")\n", "labels": {"reads": [{"table": "communityengagements", "columns": null}], "writes": [{"table": "threats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"fields\").where(\"dt = current_date()\").writeTo(\"sales_quarterly\").append()\n", "labels": {"reads": [{"table": "fields", "columns": null}], "writes": [{"table": "sales_quarterly", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"workforce\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"commodity_prices\")\n", "labels": {"reads": [{"table": "workforce", "columns": null}], "writes": [{"table": "commodity_prices", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"inclusion_efforts\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "inclusion_efforts", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"mart.mart_products_df\")\nupsert_to_output(df, \"communityevents\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_products_df", "columns": null}], "writes": [{"table": "communityevents", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"artifacts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"market_trends\")\n", "labels": {"reads": [{"table": "artifacts", "columns": null}], "writes": [{"table": "market_trends", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO wastewatertreatment SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"eventattendance\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "eventattendance", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO galleries SELECT biz_date, matchid, enrollment, show_name FROM underwater_cables WHERE biz_date > 160\"\n", "labels": {"reads": [{"table": "underwater_cables", "columns": ["biz_date", "matchid", "enrollment", "show_name"]}], "writes": [{"table": "galleries", "columns": ["biz_date", "matchid", "enrollment", "show_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table exhibitiondetails --columns bias_score,strainid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "exhibitiondetails", "columns": ["bias_score", "strainid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO nba SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO nba_games SELECT a.hours_contributed, b.dock_count FROM vehicle_data a JOIN host b ON a.total_donation_amount = b.total_donation_amount\"\n", "labels": {"reads": [{"table": "vehicle_data", "columns": null}, {"table": "host", "columns": null}], "writes": [{"table": "nba_games", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO concert SELECT a.menu_category, b.concert_id FROM hotel_revenue a JOIN view_unit_status b ON a.mission_name = b.mission_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "hotel_revenue", "columns": null}, {"table": "view_unit_status", "columns": null}], "writes": [{"table": "concert", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO fieldd_info SELECT a.injury, b.case_status FROM maintenance_requests a JOIN vessel_registry b ON a.building_id = b.building_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": null}, {"table": "vessel_registry", "columns": null}], "writes": [{"table": "fieldd_info", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO player_demographics SELECT excavation_site, subject_id, response_time FROM acceptance WHERE excavation_site > 48\")\n", "labels": {"reads": [{"table": "acceptance", "columns": ["excavation_site", "subject_id", "response_time"]}], "writes": [{"table": "player_demographics", "columns": ["excavation_site", "subject_id", "response_time"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.contract_value > 195).all()\n# src table: cargo_data\nengine.execute(\"INSERT INTO taj_mahal_visitors SELECT * FROM cargo_data\")\n", "labels": {"reads": [{"table": "cargo_data", "columns": null}], "writes": [{"table": "taj_mahal_visitors", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO diversion_programs SELECT storename, reason FROM event_attendance WHERE storename > 52\"\n", "labels": {"reads": [{"table": "event_attendance", "columns": ["storename", "reason"]}], "writes": [{"table": "diversion_programs", "columns": ["storename", "reason"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"usdaviolations\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "usdaviolations", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model labor_productivity depends on fireincidents\ndbt build -s labor_productivity --vars 'source: fireincidents'\n", "labels": {"reads": [{"table": "fireincidents", "columns": null}], "writes": [{"table": "labor_productivity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model suppliersfairlabor depends on multimodal_trips\ndbt run -s suppliersfairlabor --vars '{\"src\":\"multimodal_trips\"}'\n", "labels": {"reads": [{"table": "multimodal_trips", "columns": null}], "writes": [{"table": "suppliersfairlabor", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO vessel_positions SELECT mascot, crime_id, nominee FROM ads.risk_score WHERE mascot > 180\")\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": ["mascot", "crime_id", "nominee"]}], "writes": [{"table": "vessel_positions", "columns": ["mascot", "crime_id", "nominee"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"student_access\")\nsrc.write.insertInto(\"musical\", overwrite=True)\n", "labels": {"reads": [{"table": "student_access", "columns": null}], "writes": [{"table": "musical", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT amount_claimed, gender_mf FROM cosmetic_formula\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"healthcare\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "cosmetic_formula", "columns": ["amount_claimed", "gender_mf"]}], "writes": [{"table": "healthcare", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"genetics.crispr\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "genetics.crispr", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"permit\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"maintenance_schedule\")\n", "labels": {"reads": [{"table": "permit", "columns": null}], "writes": [{"table": "maintenance_schedule", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table yttrium_production --columns treatment_id,lastclaimdate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "yttrium_production", "columns": ["treatment_id", "lastclaimdate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"sustainable_tourism_practices\").where(\"dt = current_date()\").writeTo(\"recreation_centers\").append()\n", "labels": {"reads": [{"table": "sustainable_tourism_practices", "columns": null}], "writes": [{"table": "recreation_centers", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"financial_capability_programs\")\nsrc.write.insertInto(\"ads_payments_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "financial_capability_programs", "columns": null}], "writes": [{"table": "ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO expensive_space_missions SELECT cruelty_free, awayteamid FROM product_details WHERE cruelty_free > 315\"\n", "labels": {"reads": [{"table": "product_details", "columns": ["cruelty_free", "awayteamid"]}], "writes": [{"table": "expensive_space_missions", "columns": ["cruelty_free", "awayteamid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO fieldd_info SELECT name, contractor_name, lender_id, attendance_id FROM transportation_fleet WHERE name > 243\")\n", "labels": {"reads": [{"table": "transportation_fleet", "columns": ["name", "contractor_name", "lender_id", "attendance_id"]}], "writes": [{"table": "fieldd_info", "columns": ["name", "contractor_name", "lender_id", "attendance_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"canals\")\nsrc.write.insertInto(\"runs\", overwrite=True)\n", "labels": {"reads": [{"table": "canals", "columns": null}], "writes": [{"table": "runs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ecohousing SELECT a.away_team, b.services FROM southchinasea.wells a JOIN volunteer_registration b ON a.ship_date = b.ship_date\"\n", "labels": {"reads": [{"table": "southchinasea.wells", "columns": null}, {"table": "volunteer_registration", "columns": null}], "writes": [{"table": "ecohousing", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO product_reviews SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO traveler (trackid, shippeddate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "traveler", "columns": ["trackid", "shippeddate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model readings depends on member_attendance\ndbt run --select readings --vars 'source: member_attendance'\n", "labels": {"reads": [{"table": "member_attendance", "columns": null}], "writes": [{"table": "readings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dws_refunds_daily depends on workforce\ndbt build -s dws_refunds_daily --vars '{\"source_table\":\"workforce\"}'\n", "labels": {"reads": [{"table": "workforce", "columns": null}], "writes": [{"table": "dws_refunds_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO algorithmic_fairness_incidents_monthly SELECT eliminated_by, yearadded, sport_id, openingid FROM microfinance_clients WHERE eliminated_by > 8\")\n", "labels": {"reads": [{"table": "microfinance_clients", "columns": ["eliminated_by", "yearadded", "sport_id", "openingid"]}], "writes": [{"table": "algorithmic_fairness_incidents_monthly", "columns": ["eliminated_by", "yearadded", "sport_id", "openingid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT opened_date, song FROM education_union LIMIT 363\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "education_union", "columns": ["opened_date", "song"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO threat_intel SELECT scientist, date_problem_reported FROM ads.ads_orders WHERE scientist > 267\")\n", "labels": {"reads": [{"table": "ads.ads_orders", "columns": ["scientist", "date_problem_reported"]}], "writes": [{"table": "threat_intel", "columns": ["scientist", "date_problem_reported"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dailystreams SELECT playdate, permit_id FROM sustainable_fabrics WHERE playdate > 21\"\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": ["playdate", "permit_id"]}], "writes": [{"table": "dailystreams", "columns": ["playdate", "permit_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nimport logging\nsql = \"INSERT INTO mobile_plans SELECT a.file_size, b.contributiondate FROM team a JOIN medical_facilities_nyc b ON a.studentname = b.studentname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "team", "columns": null}, {"table": "medical_facilities_nyc", "columns": null}], "writes": [{"table": "mobile_plans", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO donation SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO volunteer_hours (fare, capacity_percentage) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "volunteer_hours", "columns": ["fare", "capacity_percentage"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"city_labor_cost\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"security_incidents\")\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": null}], "writes": [{"table": "security_incidents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table pollution_control_initiatives --columns trial_name,decision --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "pollution_control_initiatives", "columns": ["trial_name", "decision"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO marinespeciesobservations SELECT visit_id, investment_id FROM recreation_centers WHERE visit_id > 81\"\n", "labels": {"reads": [{"table": "recreation_centers", "columns": ["visit_id", "investment_id"]}], "writes": [{"table": "marinespeciesobservations", "columns": ["visit_id", "investment_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO medicine SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart_shipments_full SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model inventory_delta depends on ai_safety\ndbt build -s inventory_delta --vars '{\"source_table\":\"ai_safety\"}'\n", "labels": {"reads": [{"table": "ai_safety", "columns": null}], "writes": [{"table": "inventory_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_address_history\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"instructor\")\n", "labels": {"reads": [{"table": "customer_address_history", "columns": null}], "writes": [{"table": "instructor", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd.dwd_vendors SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"enrollments\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "enrollments", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"smartcitytech\").toPandas()\ndf[[\"carbon_footprint\", \"moisture\"]].to_sql(\"products_in_events\", engine, index=False)\n", "labels": {"reads": [{"table": "smartcitytech", "columns": null}], "writes": [{"table": "products_in_events", "columns": ["carbon_footprint", "moisture"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table green_certification --target-dir /tmp/land\n", "labels": {"reads": [{"table": "green_certification", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO well_production SELECT investor_details, num_investments, sanctuary FROM urbanagricrop WHERE investor_details > 386\"\n", "labels": {"reads": [{"table": "urbanagricrop", "columns": ["investor_details", "num_investments", "sanctuary"]}], "writes": [{"table": "well_production", "columns": ["investor_details", "num_investments", "sanctuary"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO degrees SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT co2_amount, grant_start_date FROM dws_shipments_df\", engine)\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"third_party_companies\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws_shipments_df", "columns": ["co2_amount", "grant_start_date"]}], "writes": [{"table": "third_party_companies", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"projecttimeline\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"appointment\")\n", "labels": {"reads": [{"table": "projecttimeline", "columns": null}], "writes": [{"table": "appointment", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table grants --target-dir /tmp/land\n", "labels": {"reads": [{"table": "grants", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dw.dw_users_di SELECT * FROM legacy\ncur.execute(\"SELECT protected, costid FROM textile_waste LIMIT 188\")\n", "labels": {"reads": [{"table": "textile_waste", "columns": ["protected", "costid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.risk_score_daily\").toPandas()\ndf[[\"athlete_name\", \"bias_score\"]].to_sql(\"passenger_trips\", engine, index=False)\n", "labels": {"reads": [{"table": "dws.risk_score_daily", "columns": null}], "writes": [{"table": "passenger_trips", "columns": ["athlete_name", "bias_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workshop\").toPandas()\ndf[[\"mh_id\", \"hours\"]].to_sql(\"articles_es\", engine, index=False)\n", "labels": {"reads": [{"table": "workshop", "columns": null}], "writes": [{"table": "articles_es", "columns": ["mh_id", "hours"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"creative_ai\").toPandas()\ndf[[\"director\", \"site_name\"]].to_sql(\"grad_students\", engine, index=False)\n", "labels": {"reads": [{"table": "creative_ai", "columns": null}], "writes": [{"table": "grad_students", "columns": ["director", "site_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO farmers_india SELECT market_share, founder_race, home_team_id, jan FROM certifications WHERE market_share > 155\"\n", "labels": {"reads": [{"table": "certifications", "columns": ["market_share", "founder_race", "home_team_id", "jan"]}], "writes": [{"table": "farmers_india", "columns": ["market_share", "founder_race", "home_team_id", "jan"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"appointment\").toPandas()\ndf[[\"service_details\", \"count_time\"]].to_sql(\"stg.refunds_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "appointment", "columns": null}], "writes": [{"table": "stg.refunds_daily", "columns": ["service_details", "count_time"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"weather_record\").where(\"dt = current_date()\").writeTo(\"dwd.dwd_device_log_delta\").append()\n", "labels": {"reads": [{"table": "weather_record", "columns": null}], "writes": [{"table": "dwd.dwd_device_log_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO product_revenue SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO wastedata SELECT serviceid, date_of_enrolment FROM economic_diversification_projects WHERE serviceid > 145\"\n", "labels": {"reads": [{"table": "economic_diversification_projects", "columns": ["serviceid", "date_of_enrolment"]}], "writes": [{"table": "wastedata", "columns": ["serviceid", "date_of_enrolment"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_source(ctx, \"supportprograms\")\npersist_to_target(df, \"vessel_performance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "supportprograms", "columns": null}], "writes": [{"table": "vessel_performance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO defense_personnel (activity_type, asset_make) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "defense_personnel", "columns": ["activity_type", "asset_make"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT document_id, category_id FROM creative_ai LIMIT 99\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO ancient_artifacts SELECT ethical_certifications, healthcareid FROM bi.payments_daily WHERE ethical_certifications > 330\")\n", "labels": {"reads": [{"table": "creative_ai", "columns": ["document_id", "category_id"]}, {"table": "bi.payments_daily", "columns": ["ethical_certifications", "healthcareid"]}], "writes": [{"table": "ancient_artifacts", "columns": ["ethical_certifications", "healthcareid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ai_papers\", conn)\ndf.to_sql(\"ods.inventory_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ai_papers", "columns": null}], "writes": [{"table": "ods.inventory_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"producers\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "producers", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"stars\").where(\"dt = current_date()\").writeTo(\"ods.ods_member_point_delta\").append()\n", "labels": {"reads": [{"table": "stars", "columns": null}], "writes": [{"table": "ods.ods_member_point_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO government_transparency SELECT materialtype, galleryid, policy FROM cargo_tracking WHERE materialtype > 66\"], check=True)\n", "labels": {"reads": [{"table": "cargo_tracking", "columns": ["materialtype", "galleryid", "policy"]}], "writes": [{"table": "government_transparency", "columns": ["materialtype", "galleryid", "policy"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.bi_inventory_full (jobtitle, protected) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_inventory_full", "columns": ["jobtitle", "protected"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 191;\nSQL\n", "labels": {"reads": [{"table": "spacemissions", "columns": ["trip_id", "songid"]}, {"table": "manager_award", "columns": ["address_content", "team_id_loser", "meter_100"]}], "writes": [{"table": "firestations", "columns": ["address_content", "team_id_loser", "meter_100"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO articles SELECT master_customer_id, building_full_name, hours_spent FROM bi.clicks_df WHERE master_customer_id > 396\"\n", "labels": {"reads": [{"table": "bi.clicks_df", "columns": ["master_customer_id", "building_full_name", "hours_spent"]}], "writes": [{"table": "articles", "columns": ["master_customer_id", "building_full_name", "hours_spent"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"stg.stg_inventory_hourly\");\ndf.write().mode(\"overwrite\").saveAsTable(\"urban_initiatives\");\n", "labels": {"reads": [{"table": "stg.stg_inventory_hourly", "columns": null}], "writes": [{"table": "urban_initiatives", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO furniture SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO representative SELECT a.lender_name, b.currency FROM fish_suppliers a JOIN geologicalsurvey b ON a.therapy_session = b.therapy_session\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fish_suppliers", "columns": null}, {"table": "geologicalsurvey", "columns": null}], "writes": [{"table": "representative", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO drought_impact (streamid, cropid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "drought_impact", "columns": ["streamid", "cropid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO coal SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO co2_emission SELECT job, region, number_thousands FROM guests WHERE job > 53\"\n", "labels": {"reads": [{"table": "guests", "columns": ["job", "region", "number_thousands"]}], "writes": [{"table": "co2_emission", "columns": ["job", "region", "number_thousands"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"smartcitycosts\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"tb_cases\")\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": null}], "writes": [{"table": "tb_cases", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO participants (ll_id, i_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "participants", "columns": ["ll_id", "i_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"medicine\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "medicine", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"industrial_building_energy_efficiency\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"supplychainemployees\")\n", "labels": {"reads": [{"table": "industrial_building_energy_efficiency", "columns": null}], "writes": [{"table": "supplychainemployees", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO tourismproviders SELECT ticketprice, deliveryaddress, implemented_date FROM stg.device_log_df WHERE ticketprice > 432\");\n", "labels": {"reads": [{"table": "stg.device_log_df", "columns": ["ticketprice", "deliveryaddress", "implemented_date"]}], "writes": [{"table": "tourismproviders", "columns": ["ticketprice", "deliveryaddress", "implemented_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_source(ctx, \"election\")\npush_to_store(df, \"artifact_analysis\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "election", "columns": null}], "writes": [{"table": "artifact_analysis", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"billstatus\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"customerorders\")\n", "labels": {"reads": [{"table": "billstatus", "columns": null}], "writes": [{"table": "customerorders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO mart.campaigns_full SELECT precip, register_year FROM list WHERE precip > 476\")\n", "labels": {"reads": [{"table": "list", "columns": ["precip", "register_year"]}], "writes": [{"table": "mart.campaigns_full", "columns": ["precip", "register_year"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT card_number, orderid FROM article_views LIMIT 17\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "article_views", "columns": ["card_number", "orderid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model trainings depends on satellites\ndbt run --select trainings --vars '{\"src\":\"satellites\"}'\n", "labels": {"reads": [{"table": "satellites", "columns": null}], "writes": [{"table": "trainings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM canada_cosmetics_preferences\", conn)\ndf.to_sql(\"measurements\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "canada_cosmetics_preferences", "columns": null}], "writes": [{"table": "measurements", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dws.exposure_df\").where(\"dt = current_date()\").writeTo(\"department_store_chain\").append()\n", "labels": {"reads": [{"table": "dws.exposure_df", "columns": null}], "writes": [{"table": "department_store_chain", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg.stg_products_full (drug_name, mountain_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_products_full", "columns": ["drug_name", "mountain_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"levees\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "levees", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT max_dissolved_oxygen, avg_usage FROM africa_projects LIMIT 60\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "africa_projects", "columns": ["max_dissolved_oxygen", "avg_usage"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table doctors --columns instructor,performance_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "doctors", "columns": ["instructor", "performance_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT program_name, authors FROM equipment_sales LIMIT 432\")\nrows = cur.fetchall()\nimport logging\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "equipment_sales", "columns": ["program_name", "authors"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"elimination\")\nupsert_to_warehouse(df, \"initiatives_3\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "elimination", "columns": null}], "writes": [{"table": "initiatives_3", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO light_rail_lines SELECT date_order_placed, lesson_time FROM esportsevents WHERE date_order_placed > 187\");\n", "labels": {"reads": [{"table": "esportsevents", "columns": ["date_order_placed", "lesson_time"]}], "writes": [{"table": "light_rail_lines", "columns": ["date_order_placed", "lesson_time"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM salesperson\", conn)\ndf.to_sql(\"whale_sightings\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "salesperson", "columns": null}], "writes": [{"table": "whale_sightings", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"dws_coupon_use_df\")\nsink_to_target(df, \"ods.vendors_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws_coupon_use_df", "columns": null}], "writes": [{"table": "ods.vendors_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_table(ctx, \"evidence_based_policies\")\npersist_to_store(df, \"tourism\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "evidence_based_policies", "columns": null}], "writes": [{"table": "tourism", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"maintenance_requests\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM crypto_transactions\", conn)\ndf.to_sql(\"conservation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "crypto_transactions", "columns": null}], "writes": [{"table": "conservation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"waste\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "waste", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT rural_area, service FROM player_attributes\", engine)\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"ads_payments_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "player_attributes", "columns": ["rural_area", "service"]}], "writes": [{"table": "ads_payments_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM player_award\", conn)\ndf.to_sql(\"ods_payments_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "player_award", "columns": null}], "writes": [{"table": "ods_payments_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fans\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"satellitematerials\")\n", "labels": {"reads": [{"table": "fans", "columns": null}], "writes": [{"table": "satellitematerials", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ocean_depths SELECT * FROM legacy\ncur.execute(\"SELECT currency, province_id FROM zip_codes LIMIT 352\")\n", "labels": {"reads": [{"table": "zip_codes", "columns": ["currency", "province_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 26;\nSQL\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": ["local_authority", "sellingprice"]}, {"table": "request", "columns": ["material_type", "practicename", "participant_type_code"]}], "writes": [{"table": "song", "columns": ["material_type", "practicename", "participant_type_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"country_waste_generation\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "country_waste_generation", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO jupiter_spacecraft SELECT * FROM legacy\ncur.execute(\"SELECT concert_id, strategy_name FROM maintenance_engineers LIMIT 24\")\n", "labels": {"reads": [{"table": "maintenance_engineers", "columns": ["concert_id", "strategy_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO city_properties SELECT time_id, donor_id FROM attack_outcomes WHERE time_id > 263\"\n", "labels": {"reads": [{"table": "attack_outcomes", "columns": ["time_id", "donor_id"]}], "writes": [{"table": "city_properties", "columns": ["time_id", "donor_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_risk_score_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"train_lines\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"user_genre\")\n", "labels": {"reads": [{"table": "train_lines", "columns": null}], "writes": [{"table": "user_genre", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg.risk_score_di (galleryname, workoutdate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg.risk_score_di", "columns": ["galleryname", "workoutdate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"overwatch_scores\")\nsrc.write.insertInto(\"sustainabilityratings\", overwrite=True)\n", "labels": {"reads": [{"table": "overwatch_scores", "columns": null}], "writes": [{"table": "sustainabilityratings", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attribute_name, fair_labor FROM erc20_transactions\", engine)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"mobile_usage\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": ["attribute_name", "fair_labor"]}], "writes": [{"table": "mobile_usage", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO public.police_calls SELECT home_team_three_point, comment_count, certification FROM grad_students WHERE home_team_three_point > 291\")\n", "labels": {"reads": [{"table": "grad_students", "columns": ["home_team_three_point", "comment_count", "certification"]}], "writes": [{"table": "public.police_calls", "columns": ["home_team_three_point", "comment_count", "certification"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_device_log_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"wells\")\n", "labels": {"reads": [{"table": "ods.ods_device_log_delta", "columns": null}], "writes": [{"table": "wells", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 230;\nEOF\n", "labels": {"reads": [{"table": "trade_history", "columns": ["supplychainid", "allergy", "review_score"]}], "writes": [{"table": "restaurant_revenue", "columns": ["supplychainid", "allergy", "review_score"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ods.ods_users_di SELECT * FROM legacy\nspark.sql(\"INSERT INTO labor_cost SELECT analysis_date, visitid FROM bioprocess_engineering WHERE analysis_date > 16\")\n", "labels": {"reads": [{"table": "bioprocess_engineering", "columns": ["analysis_date", "visitid"]}], "writes": [{"table": "labor_cost", "columns": ["analysis_date", "visitid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workout_sessions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"seafood\")\n", "labels": {"reads": [{"table": "workout_sessions", "columns": null}], "writes": [{"table": "seafood", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO spacecraftspeed (sportname, crop_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "spacecraftspeed", "columns": ["sportname", "crop_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ods.sessions SELECT * FROM legacy\nspark.sql(\"INSERT INTO workforce_development SELECT deliveryid, resolution, port FROM mentalhealthprofessional WHERE deliveryid > 329\")\n", "labels": {"reads": [{"table": "mentalhealthprofessional", "columns": ["deliveryid", "resolution", "port"]}], "writes": [{"table": "workforce_development", "columns": ["deliveryid", "resolution", "port"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO students_lifelong_learning SELECT artwork_id, host_city_id, fine_amount, half FROM forest WHERE artwork_id > 23\"\n", "labels": {"reads": [{"table": "forest", "columns": ["artwork_id", "host_city_id", "fine_amount", "half"]}], "writes": [{"table": "students_lifelong_learning", "columns": ["artwork_id", "host_city_id", "fine_amount", "half"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw_member_point_full\").toPandas()\ndf[[\"complaint_status_code\", \"platform_id\"]].to_sql(\"stg.stg_risk_score\", engine, index=False)\n", "labels": {"reads": [{"table": "dw_member_point_full", "columns": null}], "writes": [{"table": "stg.stg_risk_score", "columns": ["complaint_status_code", "platform_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO electricvehiclestats SELECT * FROM legacy\nspark.sql(\"INSERT INTO hospital_equipment SELECT fare_id, borough FROM sustainability_initiatives WHERE fare_id > 211\")\n", "labels": {"reads": [{"table": "sustainability_initiatives", "columns": ["fare_id", "borough"]}], "writes": [{"table": "hospital_equipment", "columns": ["fare_id", "borough"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.ods_risk_score_delta\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"mart_events_full\")\n", "labels": {"reads": [{"table": "ods.ods_risk_score_delta", "columns": null}], "writes": [{"table": "mart_events_full", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ref_locations (satellite, country_of_origin) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ref_locations", "columns": ["satellite", "country_of_origin"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_events_full\"\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 144;\nEOF\n", "labels": {"reads": [{"table": "mart.mart_users_delta", "columns": ["location_name", "issue_month", "attraction_type_description"]}], "writes": [{"table": "musicsales", "columns": ["location_name", "issue_month", "attraction_type_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"media_types\").toPandas()\ndf[[\"startup_name\", \"provider_parity_score\"]].to_sql(\"mental_health_parity_violations\", engine, index=False)\n", "labels": {"reads": [{"table": "media_types", "columns": null}], "writes": [{"table": "mental_health_parity_violations", "columns": ["startup_name", "provider_parity_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"ticketspending\")\nwrite_to_store(df, \"club\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ticketspending", "columns": null}], "writes": [{"table": "club", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO habitat_preservation SELECT * FROM legacy\ncur.execute(\"SELECT socially_responsible, restock_date FROM dwd.dwd_risk_score_delta LIMIT 243\")\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": ["socially_responsible", "restock_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cases (priceid, vehicle) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cases", "columns": ["priceid", "vehicle"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO tickets_3 SELECT has_disability, zone, eventattendance FROM skincaresales WHERE has_disability > 163\"\n", "labels": {"reads": [{"table": "skincaresales", "columns": ["has_disability", "zone", "eventattendance"]}], "writes": [{"table": "tickets_3", "columns": ["has_disability", "zone", "eventattendance"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO funding_rounds (eventname, injury) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "funding_rounds", "columns": ["eventname", "injury"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.clicks SELECT programarea, resolution FROM cities WHERE programarea > 389\"\n", "labels": {"reads": [{"table": "cities", "columns": ["programarea", "resolution"]}], "writes": [{"table": "mart.clicks", "columns": ["programarea", "resolution"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dw.events_hourly SELECT museumname, initiative_name, missions, drug_name FROM music_events WHERE museumname > 331\"\n", "labels": {"reads": [{"table": "music_events", "columns": ["museumname", "initiative_name", "missions", "drug_name"]}], "writes": [{"table": "dw.events_hourly", "columns": ["museumname", "initiative_name", "missions", "drug_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"arrivals\").where(\"dt = current_date()\").writeTo(\"cases\").append()\n", "labels": {"reads": [{"table": "arrivals", "columns": null}], "writes": [{"table": "cases", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"shipment\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"humanitarianassistanceoperations\")\n", "labels": {"reads": [{"table": "shipment", "columns": null}], "writes": [{"table": "humanitarianassistanceoperations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO food_justice_orgs SELECT a.policyname, b.publicationid FROM coral_reefs a JOIN urban_farms b ON a.dribbling = b.dribbling\"\n", "labels": {"reads": [{"table": "coral_reefs", "columns": null}, {"table": "urban_farms", "columns": null}], "writes": [{"table": "food_justice_orgs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO farms SELECT dob, vaccine_name, product_stock_number, prod_date FROM eco_materials WHERE dob > 244\"\n", "labels": {"reads": [{"table": "eco_materials", "columns": ["dob", "vaccine_name", "product_stock_number", "prod_date"]}], "writes": [{"table": "farms", "columns": ["dob", "vaccine_name", "product_stock_number", "prod_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"videos\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ref_hotel_star_ratings\")\n", "labels": {"reads": [{"table": "videos", "columns": null}], "writes": [{"table": "ref_hotel_star_ratings", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO song SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"expenses\").toPandas()\ndf[[\"attendee_id\", \"market_id\"]].to_sql(\"race\", engine, index=False)\n", "labels": {"reads": [{"table": "expenses", "columns": null}], "writes": [{"table": "race", "columns": ["attendee_id", "market_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO op_projects SELECT destroyed_by_employee_id, council_tax_id FROM marketing_regions WHERE destroyed_by_employee_id > 126\"], check=True)\n", "labels": {"reads": [{"table": "marketing_regions", "columns": ["destroyed_by_employee_id", "council_tax_id"]}], "writes": [{"table": "op_projects", "columns": ["destroyed_by_employee_id", "council_tax_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO intelligenceoperations SELECT complaintid, jobcategory FROM rental WHERE complaintid > 333\"\n", "labels": {"reads": [{"table": "rental", "columns": ["complaintid", "jobcategory"]}], "writes": [{"table": "intelligenceoperations", "columns": ["complaintid", "jobcategory"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 309;\nSQL\n", "labels": {"reads": [{"table": "fields_production", "columns": ["has_aloe_vera", "community_members"]}, {"table": "transport", "columns": ["gross_in_dollar", "rec_engine", "workouttype", "song_year"]}], "writes": [{"table": "bridgerainfall", "columns": ["gross_in_dollar", "rec_engine", "workouttype", "song_year"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 418;\nSQL\n", "labels": {"reads": [{"table": "dw.shipments_di", "columns": ["address_content", "ocean_name"]}, {"table": "program_budget", "columns": ["transaction_product", "energy_generated", "project_details"]}], "writes": [{"table": "bi.bi_payments", "columns": ["transaction_product", "energy_generated", "project_details"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO elements_price (department_id, farm_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "elements_price", "columns": ["department_id", "farm_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO esa_missions SELECT date_stored, vegetable FROM facility_production WHERE date_stored > 281\"], check=True)\n", "labels": {"reads": [{"table": "facility_production", "columns": ["date_stored", "vegetable"]}], "writes": [{"table": "esa_missions", "columns": ["date_stored", "vegetable"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO broadband_subscribers SELECT actor_id, co2_offset_amount FROM mammals WHERE actor_id > 170\")\n", "labels": {"reads": [{"table": "mammals", "columns": ["actor_id", "co2_offset_amount"]}], "writes": [{"table": "broadband_subscribers", "columns": ["actor_id", "co2_offset_amount"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"safety_data\").toPandas()\ndf[[\"green_building_id\", \"min_salary\"]].to_sql(\"directors\", engine, index=False)\n", "labels": {"reads": [{"table": "safety_data", "columns": null}], "writes": [{"table": "directors", "columns": ["green_building_id", "min_salary"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO fashion_trend_data SELECT sustainabilityrating, is_ev FROM stg.risk_score_hourly WHERE sustainabilityrating > 457\")\n", "labels": {"reads": [{"table": "stg.risk_score_hourly", "columns": ["sustainabilityrating", "is_ev"]}], "writes": [{"table": "fashion_trend_data", "columns": ["sustainabilityrating", "is_ev"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO invoice_lines SELECT system_id, workers FROM ods_vendors_daily WHERE system_id > 80\"\n", "labels": {"reads": [{"table": "ods_vendors_daily", "columns": ["system_id", "workers"]}], "writes": [{"table": "invoice_lines", "columns": ["system_id", "workers"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"textile_suppliers\").toPandas()\ndf[[\"surname\", \"years_operating\"]].to_sql(\"menu_items\", engine, index=False)\n", "labels": {"reads": [{"table": "textile_suppliers", "columns": null}], "writes": [{"table": "menu_items", "columns": ["surname", "years_operating"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO exhibition SELECT * FROM legacy\nspark.sql(\"INSERT INTO shop SELECT product_quantity, share_count, closuredate FROM discount_coupons WHERE product_quantity > 483\")\n", "labels": {"reads": [{"table": "discount_coupons", "columns": ["product_quantity", "share_count", "closuredate"]}], "writes": [{"table": "shop", "columns": ["product_quantity", "share_count", "closuredate"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO trainers SELECT a.assignment_date, b.content FROM miningwaterusage a JOIN pharmasales b ON a.hispanic = b.hispanic\"\n", "labels": {"reads": [{"table": "miningwaterusage", "columns": null}, {"table": "pharmasales", "columns": null}], "writes": [{"table": "trainers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO sustainable_projects SELECT year_founded, silver, position, reports_to FROM legal_technology_funding WHERE year_founded > 409\")\n", "labels": {"reads": [{"table": "legal_technology_funding", "columns": ["year_founded", "silver", "position", "reports_to"]}], "writes": [{"table": "sustainable_projects", "columns": ["year_founded", "silver", "position", "reports_to"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.userid > 468).all()\n# src table: recalls\nengine.execute(\"INSERT INTO stg.risk_score_hourly SELECT * FROM recalls\")\n", "labels": {"reads": [{"table": "recalls", "columns": null}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO attendees SELECT detention_type_description, channel_code, valuation FROM ads.vendors_delta WHERE detention_type_description > 131\"\n", "labels": {"reads": [{"table": "ads.vendors_delta", "columns": ["detention_type_description", "channel_code", "valuation"]}], "writes": [{"table": "attendees", "columns": ["detention_type_description", "channel_code", "valuation"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table timed_locations_of_things --columns class_code,unit_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "timed_locations_of_things", "columns": ["class_code", "unit_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO agricultural_projects SELECT curator, investor, potency, follow_up_date FROM all_programs WHERE curator > 245\");\n", "labels": {"reads": [{"table": "all_programs", "columns": ["curator", "investor", "potency", "follow_up_date"]}], "writes": [{"table": "agricultural_projects", "columns": ["curator", "investor", "potency", "follow_up_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO basketball_teams SELECT date_assigned_to, center_id, store_name FROM autonomousvehicles WHERE date_assigned_to > 240\"\n", "labels": {"reads": [{"table": "autonomousvehicles", "columns": ["date_assigned_to", "center_id", "store_name"]}], "writes": [{"table": "basketball_teams", "columns": ["date_assigned_to", "center_id", "store_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 453;\nSQL\n", "labels": {"reads": [{"table": "military_tech", "columns": ["home_team_id", "vaccine_name"]}, {"table": "esportsevents", "columns": ["watch_time", "artist_name"]}], "writes": [{"table": "procedures", "columns": ["watch_time", "artist_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table dispensaries --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dispensaries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO marine_life_data SELECT party, stu_dob, dname, rating_in_percent FROM renewable_power WHERE party > 455\");\n", "labels": {"reads": [{"table": "renewable_power", "columns": ["party", "stu_dob", "dname", "rating_in_percent"]}], "writes": [{"table": "marine_life_data", "columns": ["party", "stu_dob", "dname", "rating_in_percent"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO workshops SELECT zone_id, garmentid, subscribe_date, grape FROM dws.dws_campaigns_df WHERE zone_id > 168\"\n", "labels": {"reads": [{"table": "dws.dws_campaigns_df", "columns": ["zone_id", "garmentid", "subscribe_date", "grape"]}], "writes": [{"table": "workshops", "columns": ["zone_id", "garmentid", "subscribe_date", "grape"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table port --columns common_name,item_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "port", "columns": ["common_name", "item_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO healthcare_access_v2 SELECT activity_date, expedition_name, veteran_unemployment_rate, games FROM routes WHERE activity_date > 359\"], check=True)\n", "labels": {"reads": [{"table": "routes", "columns": ["activity_date", "expedition_name", "veteran_unemployment_rate", "games"]}], "writes": [{"table": "healthcare_access_v2", "columns": ["activity_date", "expedition_name", "veteran_unemployment_rate", "games"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM draft_copies\", conn)\ndf.to_sql(\"product_sales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "draft_copies", "columns": null}], "writes": [{"table": "product_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"vehicle\").where(\"dt = current_date()\").writeTo(\"garmentproduction\").append()\n", "labels": {"reads": [{"table": "vehicle", "columns": null}], "writes": [{"table": "garmentproduction", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO tracks SELECT * FROM legacy\nspark.sql(\"INSERT INTO police_stations SELECT culturalcompetency, did, contextid, friend FROM tree_types WHERE culturalcompetency > 67\")\n", "labels": {"reads": [{"table": "tree_types", "columns": ["culturalcompetency", "did", "contextid", "friend"]}], "writes": [{"table": "police_stations", "columns": ["culturalcompetency", "did", "contextid", "friend"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO global_sales_2022 SELECT 1\"\nlogger.info(msg)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bike_share SELECT * FROM legacy\ncur.execute(\"SELECT menuid, hotel_chain_name FROM contract_negotiations LIMIT 288\")\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": ["menuid", "hotel_chain_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO authorship SELECT part_fault_id, fault_log_entry_id, population FROM precipitation_data WHERE part_fault_id > 123\")\n", "labels": {"reads": [{"table": "precipitation_data", "columns": ["part_fault_id", "fault_log_entry_id", "population"]}], "writes": [{"table": "authorship", "columns": ["part_fault_id", "fault_log_entry_id", "population"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthprovider\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tickets_3\")\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": null}], "writes": [{"table": "tickets_3", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cosmetic_formula SELECT daily_sales, research_id, visitors, watch_time FROM vulnerabilities WHERE daily_sales > 345\"], check=True)\n", "labels": {"reads": [{"table": "vulnerabilities", "columns": ["daily_sales", "research_id", "visitors", "watch_time"]}], "writes": [{"table": "cosmetic_formula", "columns": ["daily_sales", "research_id", "visitors", "watch_time"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_contracts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"projecttimelinebybudget\")\n", "labels": {"reads": [{"table": "defense_contracts", "columns": null}], "writes": [{"table": "projecttimelinebybudget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO renewableenergy SELECT method_name, average_attendance, sensor_type FROM participants_in_events WHERE method_name > 54\"\n", "labels": {"reads": [{"table": "participants_in_events", "columns": ["method_name", "average_attendance", "sensor_type"]}], "writes": [{"table": "renewableenergy", "columns": ["method_name", "average_attendance", "sensor_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO meals SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO branch SELECT a.event, b.hispanic FROM graduates a JOIN bi_device_log_daily b ON a.main_industry = b.main_industry\"\n", "labels": {"reads": [{"table": "graduates", "columns": null}, {"table": "bi_device_log_daily", "columns": null}], "writes": [{"table": "branch", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO producers SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"auctions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "auctions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"immunization\");\ndf.write().mode(\"overwrite\").saveAsTable(\"online_travel_agency\");\n", "labels": {"reads": [{"table": "immunization", "columns": null}], "writes": [{"table": "online_travel_agency", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"county_public_safety\")\nsrc.write.insertInto(\"product_details\", overwrite=True)\n", "labels": {"reads": [{"table": "county_public_safety", "columns": null}], "writes": [{"table": "product_details", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi.device_log_hourly\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "bi.device_log_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO manufacturing_processes SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO e_scooter_trips SELECT a.num_rooms, b.fan_age FROM carbon_offset_programs a JOIN providers b ON a.rating_in_percent = b.rating_in_percent\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": null}, {"table": "providers", "columns": null}], "writes": [{"table": "e_scooter_trips", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"reporters\")\nwrite_to_sink(df, \"ref_service_types\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "reporters", "columns": null}], "writes": [{"table": "ref_service_types", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"yearly_production\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "yearly_production", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT production_value, pressure FROM socialimpactinvestments\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"watertreatmentplants\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": ["production_value", "pressure"]}], "writes": [{"table": "watertreatmentplants", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model document_structures depends on program_budget\ndbt build -s document_structures --vars 'source: program_budget'\n", "labels": {"reads": [{"table": "program_budget", "columns": null}], "writes": [{"table": "document_structures", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gardens\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "gardens", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO open_pedagogy_courses SELECT farmid, gamename, issue FROM port_visits WHERE farmid > 7\"\n", "labels": {"reads": [{"table": "port_visits", "columns": ["farmid", "gamename", "issue"]}], "writes": [{"table": "open_pedagogy_courses", "columns": ["farmid", "gamename", "issue"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO vehicle_data SELECT line_id, max_dew_point_f, low_estimate, has_disability FROM environmental_impact WHERE line_id > 33\")\n", "labels": {"reads": [{"table": "environmental_impact", "columns": ["line_id", "max_dew_point_f", "low_estimate", "has_disability"]}], "writes": [{"table": "vehicle_data", "columns": ["line_id", "max_dew_point_f", "low_estimate", "has_disability"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"green_certification\")\nsrc.write.insertInto(\"heritagesites\", overwrite=True)\n", "labels": {"reads": [{"table": "green_certification", "columns": null}], "writes": [{"table": "heritagesites", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO donor SELECT market_rate, name_first FROM customer_policies WHERE market_rate > 427\"\n", "labels": {"reads": [{"table": "customer_policies", "columns": ["market_rate", "name_first"]}], "writes": [{"table": "donor", "columns": ["market_rate", "name_first"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dw.inventory_delta SELECT a.investment_name, b.resident_id FROM mobile_customers_global a JOIN support_tickets b ON a.active_from_date = b.active_from_date\"\n", "labels": {"reads": [{"table": "mobile_customers_global", "columns": null}, {"table": "support_tickets", "columns": null}], "writes": [{"table": "dw.inventory_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO railway SELECT numcases, topic, hosts, frameworkcountry FROM freshwater_fish_farms WHERE numcases > 472\");\n", "labels": {"reads": [{"table": "freshwater_fish_farms", "columns": ["numcases", "topic", "hosts", "frameworkcountry"]}], "writes": [{"table": "railway", "columns": ["numcases", "topic", "hosts", "frameworkcountry"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"union_members\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"bi_inventory_hourly\")\n", "labels": {"reads": [{"table": "union_members", "columns": null}], "writes": [{"table": "bi_inventory_hourly", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO members SELECT invested, sector_id FROM drug_approvals WHERE invested > 234\"], check=True)\n", "labels": {"reads": [{"table": "drug_approvals", "columns": ["invested", "sector_id"]}], "writes": [{"table": "members", "columns": ["invested", "sector_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO contract_timeline SELECT host_city, spacecraft_id, transact_date FROM adaptation_projects WHERE host_city > 221\"\n", "labels": {"reads": [{"table": "adaptation_projects", "columns": ["host_city", "spacecraft_id", "transact_date"]}], "writes": [{"table": "contract_timeline", "columns": ["host_city", "spacecraft_id", "transact_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table stg.stg_clicks_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.stg_clicks_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO city_tech SELECT detention_type_code, market_id, success, sustainable_practice FROM disaster_response WHERE detention_type_code > 229\"], check=True)\n", "labels": {"reads": [{"table": "disaster_response", "columns": ["detention_type_code", "market_id", "success", "sustainable_practice"]}], "writes": [{"table": "city_tech", "columns": ["detention_type_code", "market_id", "success", "sustainable_practice"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO socially_responsible_lending SELECT a.diet, b.venue_id FROM urban_initiatives a JOIN tencel_sources b ON a.student_details = b.student_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": null}, {"table": "tencel_sources", "columns": null}], "writes": [{"table": "socially_responsible_lending", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO project_issues SELECT a.product_quantity, b.unique_founders FROM apartments a JOIN cycling b ON a.builder = b.builder\"\n", "labels": {"reads": [{"table": "apartments", "columns": null}, {"table": "cycling", "columns": null}], "writes": [{"table": "project_issues", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ocean_temperatures SELECT a.is_hybrid, b.min_age FROM artcontributors a JOIN jobs b ON a.job_title_code = b.job_title_code\"\n", "labels": {"reads": [{"table": "artcontributors", "columns": null}, {"table": "jobs", "columns": null}], "writes": [{"table": "ocean_temperatures", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO disabilityadvocacy SELECT * FROM legacy\ncur.execute(\"SELECT name_full, time_second FROM plants LIMIT 108\")\n", "labels": {"reads": [{"table": "plants", "columns": ["name_full", "time_second"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 128;\nSQL\n", "labels": {"reads": [{"table": "testtypes", "columns": ["acc_percent", "commanding_officer"]}, {"table": "recyclingratessouthamerica", "columns": ["product_price", "next_entry_id"]}], "writes": [{"table": "mission", "columns": ["product_price", "next_entry_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shipment_data (clubdesc, store_email_address) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shipment_data", "columns": ["clubdesc", "store_email_address"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO tree_species SELECT a.region_id, b.show_id FROM upgrades a JOIN platformh b ON a.feature_id = b.feature_id\"\n", "labels": {"reads": [{"table": "upgrades", "columns": null}, {"table": "platformh", "columns": null}], "writes": [{"table": "tree_species", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO building_stats SELECT a.operation_type, b.market_value FROM takes a JOIN impact_investments b ON a.product_price = b.product_price\"\n", "labels": {"reads": [{"table": "takes", "columns": null}, {"table": "impact_investments", "columns": null}], "writes": [{"table": "building_stats", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO australia_offset_programs SELECT inspection_date, completion_status, donor_country, shippingmethod FROM languagesatrisk WHERE inspection_date > 288\")\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": ["inspection_date", "completion_status", "donor_country", "shippingmethod"]}], "writes": [{"table": "australia_offset_programs", "columns": ["inspection_date", "completion_status", "donor_country", "shippingmethod"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO therapy SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model trip depends on therapists\ndbt run --select trip --vars '{\"src\":\"therapists\"}'\n", "labels": {"reads": [{"table": "therapists", "columns": null}], "writes": [{"table": "trip", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO ship SELECT a.floors, b.ship_agent_id FROM militarycyberops a JOIN continent b ON a.event_details = b.event_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "militarycyberops", "columns": null}, {"table": "continent", "columns": null}], "writes": [{"table": "ship", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"restaurants_tx\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"midwest_materials\")\n", "labels": {"reads": [{"table": "restaurants_tx", "columns": null}], "writes": [{"table": "midwest_materials", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fair_trade_brands\").toPandas()\ndf[[\"attack_date\", \"class_section\"]].to_sql(\"ai_safety\", engine, index=False)\n", "labels": {"reads": [{"table": "fair_trade_brands", "columns": null}], "writes": [{"table": "ai_safety", "columns": ["attack_date", "class_section"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table medals --columns episode_number,production_quantity --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "medals", "columns": ["episode_number", "production_quantity"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO defense_projects_sales SELECT biomass, part_id, center_name FROM pacific_ocean WHERE biomass > 63\");\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": ["biomass", "part_id", "center_name"]}], "writes": [{"table": "defense_projects_sales", "columns": ["biomass", "part_id", "center_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT servicename, conferencename FROM contractnegotiations\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"audience_demographics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "contractnegotiations", "columns": ["servicename", "conferencename"]}], "writes": [{"table": "audience_demographics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO department_stores SELECT daily_sales, venue_id, farmid, posted_at FROM drivers WHERE daily_sales > 64\")\n", "labels": {"reads": [{"table": "drivers", "columns": ["daily_sales", "venue_id", "farmid", "posted_at"]}], "writes": [{"table": "department_stores", "columns": ["daily_sales", "venue_id", "farmid", "posted_at"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO wastewater_facilities (operation_count, workername) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "wastewater_facilities", "columns": ["operation_count", "workername"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ocean (test_result, propertyid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ocean", "columns": ["test_result", "propertyid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"galleryc\").toPandas()\ndf[[\"discount\", \"faculty_id\"]].to_sql(\"mart.device_log_hourly\", engine, index=False)\n", "labels": {"reads": [{"table": "galleryc", "columns": null}], "writes": [{"table": "mart.device_log_hourly", "columns": ["discount", "faculty_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO community_events SELECT exhibitionname, average_age, workout_id, researcher_id FROM chemicals WHERE exhibitionname > 44\"\n", "labels": {"reads": [{"table": "chemicals", "columns": ["exhibitionname", "average_age", "workout_id", "researcher_id"]}], "writes": [{"table": "community_events", "columns": ["exhibitionname", "average_age", "workout_id", "researcher_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO renewableenergyprojects SELECT phase, advocate_id, min_age, sustainability_initiative_id FROM average WHERE phase > 407\"\n", "labels": {"reads": [{"table": "average", "columns": ["phase", "advocate_id", "min_age", "sustainability_initiative_id"]}], "writes": [{"table": "renewableenergyprojects", "columns": ["phase", "advocate_id", "min_age", "sustainability_initiative_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibition_visits\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"factory_workers\")\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": null}], "writes": [{"table": "factory_workers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT attraction_type_description, served_subscribers FROM ref_locations LIMIT 231\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO influencers SELECT supplier_company_id, market_id FROM manager_award WHERE supplier_company_id > 127\")\n", "labels": {"reads": [{"table": "ref_locations", "columns": ["attraction_type_description", "served_subscribers"]}, {"table": "manager_award", "columns": ["supplier_company_id", "market_id"]}], "writes": [{"table": "influencers", "columns": ["supplier_company_id", "market_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO traffic_citations SELECT * FROM legacy\nspark.sql(\"INSERT INTO wearable_metrics SELECT customer_id, service_type, water_temp FROM safetyincidents WHERE customer_id > 459\")\n", "labels": {"reads": [{"table": "safetyincidents", "columns": ["customer_id", "service_type", "water_temp"]}], "writes": [{"table": "wearable_metrics", "columns": ["customer_id", "service_type", "water_temp"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"paper_data\").toPandas()\ndf[[\"steps\", \"school\"]].to_sql(\"west_providers\", engine, index=False)\n", "labels": {"reads": [{"table": "paper_data", "columns": null}], "writes": [{"table": "west_providers", "columns": ["steps", "school"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"marathons\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"eia_schedule\")\n", "labels": {"reads": [{"table": "marathons", "columns": null}], "writes": [{"table": "eia_schedule", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT installation_date, campaign_name FROM support_tickets LIMIT 20\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "support_tickets", "columns": ["installation_date", "campaign_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"research\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "research", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi_shipments_daily SELECT claim_stage_id, range FROM policy_feedback WHERE claim_stage_id > 382\"\n", "labels": {"reads": [{"table": "policy_feedback", "columns": ["claim_stage_id", "range"]}], "writes": [{"table": "bi_shipments_daily", "columns": ["claim_stage_id", "range"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT budget, file_size FROM english_premier_league\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"genetic_research\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "english_premier_league", "columns": ["budget", "file_size"]}], "writes": [{"table": "genetic_research", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT stateid, order_quantity FROM stores LIMIT 219\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ods.clicks_delta SELECT activity_date, customer_address, incident_type, material_id FROM swimmer WHERE activity_date > 459\")\n", "labels": {"reads": [{"table": "stores", "columns": ["stateid", "order_quantity"]}, {"table": "swimmer", "columns": ["activity_date", "customer_address", "incident_type", "material_id"]}], "writes": [{"table": "ods.clicks_delta", "columns": ["activity_date", "customer_address", "incident_type", "material_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"agri_innov\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "agri_innov", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"daily_revenue\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "daily_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 207;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_payments", "columns": ["author", "funding_source", "clublocation", "min_temperature_f"]}], "writes": [{"table": "singer", "columns": ["author", "funding_source", "clublocation", "min_temperature_f"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"militarypersonnel\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"gamegenres\")\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": null}], "writes": [{"table": "gamegenres", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO materials_usage SELECT organization_id, ship_id, pet_age, daily_distance FROM colorado_river_basin WHERE organization_id > 246\"\n", "labels": {"reads": [{"table": "colorado_river_basin", "columns": ["organization_id", "ship_id", "pet_age", "daily_distance"]}], "writes": [{"table": "materials_usage", "columns": ["organization_id", "ship_id", "pet_age", "daily_distance"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT usage, claimamount FROM transport LIMIT 117\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO skincareinventory SELECT survey_id, product_quantity, friend FROM atlantic_ocean WHERE survey_id > 247\")\n", "labels": {"reads": [{"table": "transport", "columns": ["usage", "claimamount"]}, {"table": "atlantic_ocean", "columns": ["survey_id", "product_quantity", "friend"]}], "writes": [{"table": "skincareinventory", "columns": ["survey_id", "product_quantity", "friend"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"mars_spacecraft\")\ndump_to_sink(df, \"city_tech\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mars_spacecraft", "columns": null}], "writes": [{"table": "city_tech", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"feed\")\nsrc.write.insertInto(\"recalls\", overwrite=True)\n", "labels": {"reads": [{"table": "feed", "columns": null}], "writes": [{"table": "recalls", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_campaigns_daily SELECT a.date_of_completion, b.hiv FROM teaches a JOIN volume b ON a.professional_development = b.professional_development\"\n", "labels": {"reads": [{"table": "teaches", "columns": null}, {"table": "volume", "columns": null}], "writes": [{"table": "bi.bi_campaigns_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"machinery\").toPandas()\ndf[[\"daily_sales\", \"transaction_type_code\"]].to_sql(\"artist_concerts\", engine, index=False)\n", "labels": {"reads": [{"table": "machinery", "columns": null}], "writes": [{"table": "artist_concerts", "columns": ["daily_sales", "transaction_type_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT founding_location, project_education FROM rural_feeder_roads\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"marine_species_arctic_ocean\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "rural_feeder_roads", "columns": ["founding_location", "project_education"]}], "writes": [{"table": "marine_species_arctic_ocean", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"european_healthcare\").where(\"dt = current_date()\").writeTo(\"salary\").append()\n", "labels": {"reads": [{"table": "european_healthcare", "columns": null}], "writes": [{"table": "salary", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO medals SELECT negotiation_date, averagespeed FROM fish_farms WHERE negotiation_date > 335\");\n", "labels": {"reads": [{"table": "fish_farms", "columns": ["negotiation_date", "averagespeed"]}], "writes": [{"table": "medals", "columns": ["negotiation_date", "averagespeed"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT num_sessions, competition_type FROM bi_orders_daily LIMIT 55\")\nimport logging\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO virtual_tour_offers SELECT archaeologist_name, analysis_date FROM bi.bi_shipments WHERE archaeologist_name > 144\")\n", "labels": {"reads": [{"table": "bi_orders_daily", "columns": ["num_sessions", "competition_type"]}, {"table": "bi.bi_shipments", "columns": ["archaeologist_name", "analysis_date"]}], "writes": [{"table": "virtual_tour_offers", "columns": ["archaeologist_name", "analysis_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"world_heritage_sites\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"low_value_contracts\")\n", "labels": {"reads": [{"table": "world_heritage_sites", "columns": null}], "writes": [{"table": "low_value_contracts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"militarypatents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "militarypatents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO classroom SELECT personnelbranch, catalog_entry_id, safety_score, creationyear FROM tree_species WHERE personnelbranch > 186\")\n", "labels": {"reads": [{"table": "tree_species", "columns": ["personnelbranch", "catalog_entry_id", "safety_score", "creationyear"]}], "writes": [{"table": "classroom", "columns": ["personnelbranch", "catalog_entry_id", "safety_score", "creationyear"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 300;\nEOF\n", "labels": {"reads": [{"table": "passengers", "columns": ["saleamount", "ll_hours", "clinic_name", "stu_fname"]}], "writes": [{"table": "events", "columns": ["saleamount", "ll_hours", "clinic_name", "stu_fname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT reader_id, healthequitymetricscore FROM areas LIMIT 145\")\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO genetic.projects SELECT cargoid, waste_type, recipe_id, model FROM hotel_reviews WHERE cargoid > 403\")\n", "labels": {"reads": [{"table": "areas", "columns": ["reader_id", "healthequitymetricscore"]}, {"table": "hotel_reviews", "columns": ["cargoid", "waste_type", "recipe_id", "model"]}], "writes": [{"table": "genetic.projects", "columns": ["cargoid", "waste_type", "recipe_id", "model"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO union_membership (grade, operation_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "union_membership", "columns": ["grade", "operation_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 96;\nSQL\n", "labels": {"reads": [{"table": "sportsinfo", "columns": ["dept_store_chain_id", "game_name"]}, {"table": "climate_finance_organizations", "columns": ["amount_claimed", "salary", "order_id"]}], "writes": [{"table": "financialwellbeing", "columns": ["amount_claimed", "salary", "order_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table hotel_tech_adoption --columns fish_population,book_title --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "hotel_tech_adoption", "columns": ["fish_population", "book_title"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"route_fares\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"canada_tech\")\n", "labels": {"reads": [{"table": "route_fares", "columns": null}], "writes": [{"table": "canada_tech", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO fairness_scores SELECT a.genre_is, b.passenger_count FROM fish_feed_factories a JOIN ads_payments_hourly b ON a.dphone = b.dphone\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fish_feed_factories", "columns": null}, {"table": "ads_payments_hourly", "columns": null}], "writes": [{"table": "fairness_scores", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO ads.ads_shipments_delta SELECT a.room, b.client_first_name FROM climate_investments a JOIN workforce b ON a.pediatrician_id = b.pediatrician_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "climate_investments", "columns": null}, {"table": "workforce", "columns": null}], "writes": [{"table": "ads.ads_shipments_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model voyages depends on community_programs\ndbt build -s voyages --vars '{\"src\":\"community_programs\"}'\n", "labels": {"reads": [{"table": "community_programs", "columns": null}], "writes": [{"table": "voyages", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stars SELECT * FROM legacy\ncur.execute(\"SELECT fan_name, staff_address_id FROM all_programs LIMIT 300\")\n", "labels": {"reads": [{"table": "all_programs", "columns": ["fan_name", "staff_address_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"farmers\").where(\"dt = current_date()\").writeTo(\"mart_refunds_delta\").append()\n", "labels": {"reads": [{"table": "farmers", "columns": null}], "writes": [{"table": "mart_refunds_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_inventory_di --columns teamname,patientid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_inventory_di", "columns": ["teamname", "patientid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ads.inventory_di SELECT local, show_name, tournament_name, attorney_id FROM urban_farms WHERE local > 191\")\n", "labels": {"reads": [{"table": "urban_farms", "columns": ["local", "show_name", "tournament_name", "attorney_id"]}], "writes": [{"table": "ads.inventory_di", "columns": ["local", "show_name", "tournament_name", "attorney_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO tech_workers_union SELECT hourid, emissions, taskdate FROM bi.refunds_daily WHERE hourid > 314\");\n", "labels": {"reads": [{"table": "bi.refunds_daily", "columns": ["hourid", "emissions", "taskdate"]}], "writes": [{"table": "tech_workers_union", "columns": ["hourid", "emissions", "taskdate"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO retailerg SELECT * FROM legacy\ncur.execute(\"SELECT artifact_weight, sensor_reading FROM ads.events LIMIT 186\")\n", "labels": {"reads": [{"table": "ads.events", "columns": ["artifact_weight", "sensor_reading"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO drugs SELECT time_stamp, violation_count, status_code, numcases FROM casesbyyear WHERE time_stamp > 45\"\n", "labels": {"reads": [{"table": "casesbyyear", "columns": ["time_stamp", "violation_count", "status_code", "numcases"]}], "writes": [{"table": "drugs", "columns": ["time_stamp", "violation_count", "status_code", "numcases"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"apartments\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "apartments", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO time_dim SELECT publication_id, dates_active, rig_name, color_code FROM defense_diplomacy WHERE publication_id > 385\")\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": ["publication_id", "dates_active", "rig_name", "color_code"]}], "writes": [{"table": "time_dim", "columns": ["publication_id", "dates_active", "rig_name", "color_code"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO apartment_bookings SELECT * FROM legacy\ncur.execute(\"SELECT volunteer_name, safetytestdate FROM recall_reports LIMIT 367\")\n", "labels": {"reads": [{"table": "recall_reports", "columns": ["volunteer_name", "safetytestdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO public.crime_types SELECT * FROM legacy\ncur.execute(\"SELECT ngo_id, appointmentid FROM ods.ods_campaigns_delta LIMIT 255\")\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": ["ngo_id", "appointmentid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO low_value_contracts SELECT a.screening, b.location_id FROM dws.dws_inventory_hourly a JOIN foodsafetyrecords b ON a.zipcode = b.zipcode\"\n", "labels": {"reads": [{"table": "dws.dws_inventory_hourly", "columns": null}, {"table": "foodsafetyrecords", "columns": null}], "writes": [{"table": "low_value_contracts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 2;\nSQL\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": ["denomination", "profession_count"]}, {"table": "volunteerprograms", "columns": ["surname", "problem_log_id", "opening_year"]}], "writes": [{"table": "food_justice", "columns": ["surname", "problem_log_id", "opening_year"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO streams SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO smart_cities SELECT a.promotiondate, b.amenity_name FROM military_personnel_africa a JOIN patients b ON a.missiontype = b.missiontype\"\n", "labels": {"reads": [{"table": "military_personnel_africa", "columns": null}, {"table": "patients", "columns": null}], "writes": [{"table": "smart_cities", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"cyber_incidents\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ai_papers\")\n", "labels": {"reads": [{"table": "cyber_incidents", "columns": null}], "writes": [{"table": "ai_papers", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO portfolios SELECT * FROM legacy\ncur.execute(\"SELECT end_speed, session_date FROM member LIMIT 388\")\n", "labels": {"reads": [{"table": "member", "columns": ["end_speed", "session_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO project_issues SELECT a.building_address, b.coach_id FROM ocean_temperatures a JOIN average b ON a.dispensaryid = b.dispensaryid\"\n", "labels": {"reads": [{"table": "ocean_temperatures", "columns": null}, {"table": "average", "columns": null}], "writes": [{"table": "project_issues", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"locations\").toPandas()\ndf[[\"institution\", \"relationship\"]].to_sql(\"recycled_polyester\", engine, index=False)\n", "labels": {"reads": [{"table": "locations", "columns": null}], "writes": [{"table": "recycled_polyester", "columns": ["institution", "relationship"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT shoe_brand, artistname FROM ads.member_point\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\ndf.to_sql(\"sustainability_metrics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.member_point", "columns": ["shoe_brand", "artistname"]}], "writes": [{"table": "sustainability_metrics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 222;\nSQL\n", "labels": {"reads": [{"table": "steps", "columns": ["round_amount", "hourid"]}, {"table": "investment", "columns": ["vendorname", "participated_in_open_pedagogy"]}], "writes": [{"table": "astronauts", "columns": ["vendorname", "participated_in_open_pedagogy"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"instructor\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dw.dw_member_point_hourly\")\n", "labels": {"reads": [{"table": "instructor", "columns": null}], "writes": [{"table": "dw.dw_member_point_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO civilcases SELECT employment_id, coupon_id FROM address WHERE employment_id > 464\")\n", "labels": {"reads": [{"table": "address", "columns": ["employment_id", "coupon_id"]}], "writes": [{"table": "civilcases", "columns": ["employment_id", "coupon_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table epl_teams --columns model_name,building_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "epl_teams", "columns": ["model_name", "building_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO socially_responsible_lending SELECT * FROM legacy\ncur.execute(\"SELECT produceid, advocate_id FROM animal_species LIMIT 241\")\n", "labels": {"reads": [{"table": "animal_species", "columns": ["produceid", "advocate_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO crops_year SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table fundings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "fundings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO defense_contracts_v2 SELECT debate_id, currency FROM bridges WHERE debate_id > 203\")\n", "labels": {"reads": [{"table": "bridges", "columns": ["debate_id", "currency"]}], "writes": [{"table": "defense_contracts_v2", "columns": ["debate_id", "currency"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO chemical_composition SELECT element, caloric_content, unit_of_measure FROM bi.bi_exposure_hourly WHERE element > 202\")\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": ["element", "caloric_content", "unit_of_measure"]}], "writes": [{"table": "chemical_composition", "columns": ["element", "caloric_content", "unit_of_measure"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO humanitarian_operations SELECT bias_score, num_songs FROM mart_refunds_delta WHERE bias_score > 189\"\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": ["bias_score", "num_songs"]}], "writes": [{"table": "humanitarian_operations", "columns": ["bias_score", "num_songs"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO spacemissions SELECT hotel_name, contractid FROM league WHERE hotel_name > 306\"\n", "labels": {"reads": [{"table": "league", "columns": ["hotel_name", "contractid"]}], "writes": [{"table": "spacemissions", "columns": ["hotel_name", "contractid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"materials_usage\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"player\")\n", "labels": {"reads": [{"table": "materials_usage", "columns": null}], "writes": [{"table": "player", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workouts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"coal_reserves\")\n", "labels": {"reads": [{"table": "workouts", "columns": null}], "writes": [{"table": "coal_reserves", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 248;\nSQL\n", "labels": {"reads": [{"table": "customer_month", "columns": ["community_center_id", "quantity"]}, {"table": "scan_dates", "columns": ["post_id", "environmental_impact_score"]}], "writes": [{"table": "funding_rounds", "columns": ["post_id", "environmental_impact_score"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO skincaresales SELECT port_name, doctor_id, physical FROM artprograms WHERE port_name > 421\")\n", "labels": {"reads": [{"table": "artprograms", "columns": ["port_name", "doctor_id", "physical"]}], "writes": [{"table": "skincaresales", "columns": ["port_name", "doctor_id", "physical"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO recycled_polyester (health_equity_metric_2, installation_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "recycled_polyester", "columns": ["health_equity_metric_2", "installation_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ods.ods_risk_score_df\").where(\"dt = current_date()\").writeTo(\"dws.payments_delta\").append()\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": null}], "writes": [{"table": "dws.payments_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT model, formats FROM experts LIMIT 483\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO steps SELECT cases, loan_id, bats FROM singer_in_concert WHERE cases > 206\")\n", "labels": {"reads": [{"table": "experts", "columns": ["model", "formats"]}, {"table": "singer_in_concert", "columns": ["cases", "loan_id", "bats"]}], "writes": [{"table": "steps", "columns": ["cases", "loan_id", "bats"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stock_levels SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO shipment SELECT frameworkcountry, menu_id, operation_id, birth_place FROM check_ins WHERE frameworkcountry > 102\")\n", "labels": {"reads": [{"table": "check_ins", "columns": ["frameworkcountry", "menu_id", "operation_id", "birth_place"]}], "writes": [{"table": "shipment", "columns": ["frameworkcountry", "menu_id", "operation_id", "birth_place"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO habitats SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"labor_unions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "labor_unions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO sustainable_tourism_practices SELECT clean_jerk, played, sitename, departmentname FROM cotton_source WHERE clean_jerk > 87\");\n", "labels": {"reads": [{"table": "cotton_source", "columns": ["clean_jerk", "played", "sitename", "departmentname"]}], "writes": [{"table": "sustainable_tourism_practices", "columns": ["clean_jerk", "played", "sitename", "departmentname"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation_per_country\").toPandas()\ndf[[\"g_name\", \"fair_trade\"]].to_sql(\"park\", engine, index=False)\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": null}], "writes": [{"table": "park", "columns": ["g_name", "fair_trade"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.mart_users_di\", conn)\ndf.to_sql(\"legal_technology_funding\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": null}], "writes": [{"table": "legal_technology_funding", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO latam_schema.education_budget SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO train_lines SELECT animal_name, material_id, driverid, other_details FROM ods.coupon_use WHERE animal_name > 49\"], check=True)\n", "labels": {"reads": [{"table": "ods.coupon_use", "columns": ["animal_name", "material_id", "driverid", "other_details"]}], "writes": [{"table": "train_lines", "columns": ["animal_name", "material_id", "driverid", "other_details"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO investments_esg SELECT claimtype, trial_year FROM premises WHERE claimtype > 185\")\n", "labels": {"reads": [{"table": "premises", "columns": ["claimtype", "trial_year"]}], "writes": [{"table": "investments_esg", "columns": ["claimtype", "trial_year"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO roller_coaster SELECT a.posted_at, b.total_horses FROM portfolios a JOIN ocean_floor b ON a.thing_id = b.thing_id\"\n", "labels": {"reads": [{"table": "portfolios", "columns": null}, {"table": "ocean_floor", "columns": null}], "writes": [{"table": "roller_coaster", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO green_projects SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"investor_activities\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cultural_events SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table mental_health_parity_violations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"healthydelights\").where(\"dt = current_date()\").writeTo(\"infection_rates\").append()\n", "labels": {"reads": [{"table": "healthydelights", "columns": null}], "writes": [{"table": "infection_rates", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO landfill_capacity SELECT program, authid FROM police_emergencies WHERE program > 59\"\n", "labels": {"reads": [{"table": "police_emergencies", "columns": ["program", "authid"]}], "writes": [{"table": "landfill_capacity", "columns": ["program", "authid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO vehicledata SELECT arrival_date, lieutenant_governor, productiondate FROM attendance WHERE arrival_date > 23\"], check=True)\n", "labels": {"reads": [{"table": "attendance", "columns": ["arrival_date", "lieutenant_governor", "productiondate"]}], "writes": [{"table": "vehicledata", "columns": ["arrival_date", "lieutenant_governor", "productiondate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO species_forests SELECT login_name, claim_status_description, ironid, has_access FROM policyadvocacyevents WHERE login_name > 207\"\n", "labels": {"reads": [{"table": "policyadvocacyevents", "columns": ["login_name", "claim_status_description", "ironid", "has_access"]}], "writes": [{"table": "species_forests", "columns": ["login_name", "claim_status_description", "ironid", "has_access"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"virtual_tour_engagement\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"warehouses\")\n", "labels": {"reads": [{"table": "virtual_tour_engagement", "columns": null}], "writes": [{"table": "warehouses", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO hospitals SELECT budget_type_description, spf_level FROM reverselogisticstransactions WHERE budget_type_description > 389\"\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": ["budget_type_description", "spf_level"]}], "writes": [{"table": "hospitals", "columns": ["budget_type_description", "spf_level"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table department_store_chain --columns operation_name,quarter --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "department_store_chain", "columns": ["operation_name", "quarter"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mining_operation\"\n", "labels": {"reads": [{"table": "mining_operation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT program_id, area_type FROM classicgame LIMIT 343\")\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO judges SELECT feedid, concertid, low_estimate FROM textile_waste WHERE feedid > 57\")\n", "labels": {"reads": [{"table": "classicgame", "columns": ["program_id", "area_type"]}, {"table": "textile_waste", "columns": ["feedid", "concertid", "low_estimate"]}], "writes": [{"table": "judges", "columns": ["feedid", "concertid", "low_estimate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"suburbs\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"user_profiles\")\n", "labels": {"reads": [{"table": "suburbs", "columns": null}], "writes": [{"table": "user_profiles", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM phishing_targets\", conn)\ndf.to_sql(\"athletes\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "phishing_targets", "columns": null}], "writes": [{"table": "athletes", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 253;\nSQL\n", "labels": {"reads": [{"table": "resilience_infrastructure", "columns": ["co_id", "acc_type"]}, {"table": "dws.dws_cart_item_daily", "columns": ["crispr_id", "mineral", "storeid"]}], "writes": [{"table": "shipmentinfo", "columns": ["crispr_id", "mineral", "storeid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ods.sessions (payment_method, form_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ods.sessions", "columns": ["payment_method", "form_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"league_x\")\nsrc.write.insertInto(\"program_history\", overwrite=True)\n", "labels": {"reads": [{"table": "league_x", "columns": null}], "writes": [{"table": "program_history", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO autoshow SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO endowment SELECT a.last_checkup_date, b.garment_material FROM military_equipment a JOIN algorithmic_fairness b ON a.common_name = b.common_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "military_equipment", "columns": null}, {"table": "algorithmic_fairness", "columns": null}], "writes": [{"table": "endowment", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO broadband_plans SELECT date_of_latest_revision, station_id, bioprocess_id, employee FROM co2_emissions WHERE date_of_latest_revision > 458\"], check=True)\n", "labels": {"reads": [{"table": "co2_emissions", "columns": ["date_of_latest_revision", "station_id", "bioprocess_id", "employee"]}], "writes": [{"table": "broadband_plans", "columns": ["date_of_latest_revision", "station_id", "bioprocess_id", "employee"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO factory_water SELECT supplychainid, export_country, customer_last_name, cropid FROM trips WHERE supplychainid > 405\")\n", "labels": {"reads": [{"table": "trips", "columns": ["supplychainid", "export_country", "customer_last_name", "cropid"]}], "writes": [{"table": "factory_water", "columns": ["supplychainid", "export_country", "customer_last_name", "cropid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dates\");\ndf.write().mode(\"overwrite\").saveAsTable(\"match_season\");\n", "labels": {"reads": [{"table": "dates", "columns": null}], "writes": [{"table": "match_season", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM restorative_justice_3\", conn)\ndf.to_sql(\"military_aircraft_maintenance\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "restorative_justice_3", "columns": null}], "writes": [{"table": "military_aircraft_maintenance", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dws_clicks_di SELECT container_count, artistname, sea FROM pacific_ocean WHERE container_count > 415\"\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": ["container_count", "artistname", "sea"]}], "writes": [{"table": "dws_clicks_di", "columns": ["container_count", "artistname", "sea"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT promotiondate, individual_first_name FROM product_revenue\", engine)\nimport logging\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"volunteer_registration\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "product_revenue", "columns": ["promotiondate", "individual_first_name"]}], "writes": [{"table": "volunteer_registration", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO city.community_policing SELECT shipment_year, warehouse_state FROM mining_operation WHERE shipment_year > 304\");\n", "labels": {"reads": [{"table": "mining_operation", "columns": ["shipment_year", "warehouse_state"]}], "writes": [{"table": "city.community_policing", "columns": ["shipment_year", "warehouse_state"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ref_colors SELECT is_unionized, creationyear FROM industry_funding WHERE is_unionized > 473\")\n", "labels": {"reads": [{"table": "industry_funding", "columns": ["is_unionized", "creationyear"]}], "writes": [{"table": "ref_colors", "columns": ["is_unionized", "creationyear"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO cities SELECT * FROM legacy\ncur.execute(\"SELECT number_of_platforms, savingsid FROM collectivebargaining LIMIT 106\")\n", "labels": {"reads": [{"table": "collectivebargaining", "columns": ["number_of_platforms", "savingsid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dorm_amenity\", conn)\ndf.to_sql(\"pollution_control_initiatives\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dorm_amenity", "columns": null}], "writes": [{"table": "pollution_control_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO financial_capability_id SELECT a.coal_reserve_remaining, b.period FROM concert_revenue a JOIN hotel_chains b ON a.wage = b.wage\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "concert_revenue", "columns": null}, {"table": "hotel_chains", "columns": null}], "writes": [{"table": "financial_capability_id", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.member_point\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"professional_development\")\n", "labels": {"reads": [{"table": "ads.member_point", "columns": null}], "writes": [{"table": "professional_development", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO ods.ods_device_log_delta SELECT hours_contributed, half, sales, budgeted FROM dwd.dwd_vendors WHERE hours_contributed > 383\");\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": ["hours_contributed", "half", "sales", "budgeted"]}], "writes": [{"table": "ods.ods_device_log_delta", "columns": ["hours_contributed", "half", "sales", "budgeted"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO student_addresses SELECT college_location, emp_fname, support_id FROM government_transparency WHERE college_location > 136\"\n", "labels": {"reads": [{"table": "government_transparency", "columns": ["college_location", "emp_fname", "support_id"]}], "writes": [{"table": "student_addresses", "columns": ["college_location", "emp_fname", "support_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO attendees SELECT * FROM legacy\ncur.execute(\"SELECT age, time_month FROM urban_transportation LIMIT 449\")\n", "labels": {"reads": [{"table": "urban_transportation", "columns": ["age", "time_month"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM excavations\"\n", "labels": {"reads": [{"table": "excavations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"makeup_sales\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "makeup_sales", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO coffee_prices SELECT a.patient, b.attorney FROM euro_champs_track_field a JOIN manufacturing_processes b ON a.song_release_year = b.song_release_year\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "euro_champs_track_field", "columns": null}, {"table": "manufacturing_processes", "columns": null}], "writes": [{"table": "coffee_prices", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO az_drought_impact SELECT preferred_foot, unitprice, inclusive, entrydate FROM legal_aid_providers WHERE preferred_foot > 194\");\n", "labels": {"reads": [{"table": "legal_aid_providers", "columns": ["preferred_foot", "unitprice", "inclusive", "entrydate"]}], "writes": [{"table": "az_drought_impact", "columns": ["preferred_foot", "unitprice", "inclusive", "entrydate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"carbon_prices_3\")\nsrc.write.insertInto(\"dwd_sessions_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": null}], "writes": [{"table": "dwd_sessions_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dw.dw_orders_hourly SELECT invoice_date, lesson_time, major, oppose_rate FROM inclusivehousing.affordablehousing WHERE invoice_date > 457\"\n", "labels": {"reads": [{"table": "inclusivehousing.affordablehousing", "columns": ["invoice_date", "lesson_time", "major", "oppose_rate"]}], "writes": [{"table": "dw.dw_orders_hourly", "columns": ["invoice_date", "lesson_time", "major", "oppose_rate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO clothingsales SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO savings_programs SELECT functional_area_description, postal_code, mission_name, ota_name FROM residents_services WHERE functional_area_description > 34\"\n", "labels": {"reads": [{"table": "residents_services", "columns": ["functional_area_description", "postal_code", "mission_name", "ota_name"]}], "writes": [{"table": "savings_programs", "columns": ["functional_area_description", "postal_code", "mission_name", "ota_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"space_agencies_2\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"workforcediversity\")\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": null}], "writes": [{"table": "workforcediversity", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"player_college\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "player_college", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO haircaresales SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"assessment_notes\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "assessment_notes", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO student_access (class_room, updatedate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "student_access", "columns": ["class_room", "updatedate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO university SELECT album, retailer_name, document_type_code, product_quantity FROM mart.mart_products_hourly WHERE album > 143\")\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": ["album", "retailer_name", "document_type_code", "product_quantity"]}], "writes": [{"table": "university", "columns": ["album", "retailer_name", "document_type_code", "product_quantity"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO financial_transactions SELECT sale_quantity, preference_rating FROM public_transportation_sydney WHERE sale_quantity > 268\")\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": ["sale_quantity", "preference_rating"]}], "writes": [{"table": "financial_transactions", "columns": ["sale_quantity", "preference_rating"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO shops SELECT * FROM legacy\nspark.sql(\"INSERT INTO carbon_prices_3 SELECT fleet_id, residence, login_name FROM dwd.products_di WHERE fleet_id > 406\")\n", "labels": {"reads": [{"table": "dwd.products_di", "columns": ["fleet_id", "residence", "login_name"]}], "writes": [{"table": "carbon_prices_3", "columns": ["fleet_id", "residence", "login_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT popularity, excavation_site FROM seafoodsouthafricakenya LIMIT 30\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO trafficviolations SELECT reported, type_of_thing_code, salesperson_id FROM ads.ads_device_log_di WHERE reported > 45\")\n", "labels": {"reads": [{"table": "seafoodsouthafricakenya", "columns": ["popularity", "excavation_site"]}, {"table": "ads.ads_device_log_di", "columns": ["reported", "type_of_thing_code", "salesperson_id"]}], "writes": [{"table": "trafficviolations", "columns": ["reported", "type_of_thing_code", "salesperson_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_vendors_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods_vendors_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dailystreams\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"water_treatment_facilities\")\n", "labels": {"reads": [{"table": "dailystreams", "columns": null}], "writes": [{"table": "water_treatment_facilities", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM cybersecurity_incidents\"\n", "labels": {"reads": [{"table": "cybersecurity_incidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT source_system_code, bname FROM flight_safety LIMIT 430\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "flight_safety", "columns": ["source_system_code", "bname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO mentalhealthprovider SELECT * FROM legacy\nspark.sql(\"INSERT INTO invoice_lines SELECT reo_type, device_name, outcome_code FROM vesselfuel WHERE reo_type > 496\")\n", "labels": {"reads": [{"table": "vesselfuel", "columns": ["reo_type", "device_name", "outcome_code"]}], "writes": [{"table": "invoice_lines", "columns": ["reo_type", "device_name", "outcome_code"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"military_sales\").where(\"dt = current_date()\").writeTo(\"support_groups\").append()\n", "labels": {"reads": [{"table": "military_sales", "columns": null}], "writes": [{"table": "support_groups", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO waste_generation_metrics SELECT class_section, safety_score FROM branch WHERE class_section > 349\")\n", "labels": {"reads": [{"table": "branch", "columns": ["class_section", "safety_score"]}], "writes": [{"table": "waste_generation_metrics", "columns": ["class_section", "safety_score"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO program_funding_2 SELECT a.report_type, b.cause_id FROM genre a JOIN ref_service_types b ON a.runtime = b.runtime\"\n", "labels": {"reads": [{"table": "genre", "columns": null}, {"table": "ref_service_types", "columns": null}], "writes": [{"table": "program_funding_2", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_table(ctx, \"flights\")\npersist_to_warehouse(df, \"climate_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "flights", "columns": null}], "writes": [{"table": "climate_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT residence, friend FROM ytterbiumproduction\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"debris\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ytterbiumproduction", "columns": ["residence", "friend"]}], "writes": [{"table": "debris", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO inspectiondata (transaction_product, competition_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "inspectiondata", "columns": ["transaction_product", "competition_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mineral_extraction SELECT account_details, total_value_purchased, museum FROM autoshows WHERE account_details > 110\"], check=True)\n", "labels": {"reads": [{"table": "autoshows", "columns": ["account_details", "total_value_purchased", "museum"]}], "writes": [{"table": "mineral_extraction", "columns": ["account_details", "total_value_purchased", "museum"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"weather\").toPandas()\ndf[[\"assignment_date\", \"venue_id\"]].to_sql(\"student_access\", engine, index=False)\n", "labels": {"reads": [{"table": "weather", "columns": null}], "writes": [{"table": "student_access", "columns": ["assignment_date", "venue_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dishes SELECT launch_date, number_of_sightings, stocking_density FROM esa_missions WHERE launch_date > 201\"\n", "labels": {"reads": [{"table": "esa_missions", "columns": ["launch_date", "number_of_sightings", "stocking_density"]}], "writes": [{"table": "dishes", "columns": ["launch_date", "number_of_sightings", "stocking_density"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO operation SELECT data_usage, manager_id, negotiation_date FROM euro_champs_track_field WHERE data_usage > 387\"\n", "labels": {"reads": [{"table": "euro_champs_track_field", "columns": ["data_usage", "manager_id", "negotiation_date"]}], "writes": [{"table": "operation", "columns": ["data_usage", "manager_id", "negotiation_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table rural_economy_2 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rural_economy_2", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 160;\nSQL\n", "labels": {"reads": [{"table": "payments", "columns": ["song_year", "savings"]}, {"table": "sustainable_tourism_practices", "columns": ["coownerid", "metric", "assets"]}], "writes": [{"table": "train_lines", "columns": ["coownerid", "metric", "assets"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT number_deaths, active_to_date FROM accounts LIMIT 440\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "accounts", "columns": ["number_deaths", "active_to_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO community_engagement SELECT productcategory, task, recipient_id FROM green_building_projects WHERE productcategory > 49\"\n", "labels": {"reads": [{"table": "green_building_projects", "columns": ["productcategory", "task", "recipient_id"]}], "writes": [{"table": "community_engagement", "columns": ["productcategory", "task", "recipient_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 405;\nEOF\n", "labels": {"reads": [{"table": "takes", "columns": ["datetime_detention_start", "team_id", "ship_agent_id"]}], "writes": [{"table": "virtual_tourism", "columns": ["datetime_detention_start", "team_id", "ship_agent_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO vessel_capacity SELECT dish_name, violationid, consider_rate FROM exhibitionattendance WHERE dish_name > 192\"\n", "labels": {"reads": [{"table": "exhibitionattendance", "columns": ["dish_name", "violationid", "consider_rate"]}], "writes": [{"table": "vessel_capacity", "columns": ["dish_name", "violationid", "consider_rate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nsql = \"INSERT INTO inclusivehousingpolicies SELECT a.purchaseid, b.aid FROM tech_workers_union a JOIN laborstatistics b ON a.event_id = b.event_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": null}, {"table": "laborstatistics", "columns": null}], "writes": [{"table": "inclusivehousingpolicies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO whale_sightings SELECT practicename, salinity FROM product_characteristics WHERE practicename > 480\");\n", "labels": {"reads": [{"table": "product_characteristics", "columns": ["practicename", "salinity"]}], "writes": [{"table": "whale_sightings", "columns": ["practicename", "salinity"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"flu_cases\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "flu_cases", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.master_customer_id > 305).all()\n# src table: visitordemographics\nengine.execute(\"INSERT INTO mental_health_parity SELECT * FROM visitordemographics\")\n", "labels": {"reads": [{"table": "visitordemographics", "columns": null}], "writes": [{"table": "mental_health_parity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO papers SELECT * FROM legacy\nspark.sql(\"INSERT INTO driver SELECT serviceid, feature_details, playergameid FROM supply_chain WHERE serviceid > 277\")\n", "labels": {"reads": [{"table": "supply_chain", "columns": ["serviceid", "feature_details", "playergameid"]}], "writes": [{"table": "driver", "columns": ["serviceid", "feature_details", "playergameid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"oceania_countries\").where(\"dt = current_date()\").writeTo(\"item\").append()\n", "labels": {"reads": [{"table": "oceania_countries", "columns": null}], "writes": [{"table": "item", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"conservation_initiatives\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "conservation_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_sessions_delta\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"smartcitycosts\")\n", "labels": {"reads": [{"table": "dw.dw_sessions_delta", "columns": null}], "writes": [{"table": "smartcitycosts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO festival_detail SELECT a.contract_start, b.mental_health_rating FROM esports_teams a JOIN ads.ads_users_hourly b ON a.treasurer_vote = b.treasurer_vote\"\n", "labels": {"reads": [{"table": "esports_teams", "columns": null}, {"table": "ads.ads_users_hourly", "columns": null}], "writes": [{"table": "festival_detail", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT developer, alid FROM ods.ods_campaigns_df\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"water_usage\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_df", "columns": ["developer", "alid"]}], "writes": [{"table": "water_usage", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table all_star --target-dir /tmp/land\n", "labels": {"reads": [{"table": "all_star", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"strains\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"textile_suppliers\")\n", "labels": {"reads": [{"table": "strains", "columns": null}], "writes": [{"table": "textile_suppliers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO space_missions SELECT * FROM legacy\nspark.sql(\"INSERT INTO user_likes SELECT mining_operation, catalog_id, fundingid, potency FROM textile_sourcing WHERE mining_operation > 344\")\n", "labels": {"reads": [{"table": "textile_sourcing", "columns": ["mining_operation", "catalog_id", "fundingid", "potency"]}], "writes": [{"table": "user_likes", "columns": ["mining_operation", "catalog_id", "fundingid", "potency"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dw.dw_member_point_di SELECT skill_description, fare_date, county, denomination FROM plankton WHERE skill_description > 27\"\n", "labels": {"reads": [{"table": "plankton", "columns": ["skill_description", "fare_date", "county", "denomination"]}], "writes": [{"table": "dw.dw_member_point_di", "columns": ["skill_description", "fare_date", "county", "denomination"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"healthcare_centers\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model securityincidents depends on shark_biomass\ndbt build -s securityincidents --vars '{\"src\":\"shark_biomass\"}'\n", "labels": {"reads": [{"table": "shark_biomass", "columns": null}], "writes": [{"table": "securityincidents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO initiative_types SELECT improvement, license_number, factory_name, ram_mib FROM gamesales WHERE improvement > 212\")\n", "labels": {"reads": [{"table": "gamesales", "columns": ["improvement", "license_number", "factory_name", "ram_mib"]}], "writes": [{"table": "initiative_types", "columns": ["improvement", "license_number", "factory_name", "ram_mib"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO supplychainemployees (major, component_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "supplychainemployees", "columns": ["major", "component_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"autonomousvehicles\");\ndf.write().mode(\"overwrite\").saveAsTable(\"stg.risk_score_hourly\");\n", "labels": {"reads": [{"table": "autonomousvehicles", "columns": null}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"biosensors.projects\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ads.ads_users_hourly\");\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": null}], "writes": [{"table": "ads.ads_users_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"ota_revenue\")\npersist_to_output(df, \"autoshows\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ota_revenue", "columns": null}], "writes": [{"table": "autoshows", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model roller_coaster depends on regulatory_frameworks\ndbt run --select roller_coaster --vars '{\"source_table\":\"regulatory_frameworks\"}'\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": null}], "writes": [{"table": "roller_coaster", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"attendees\");\ndf.write().mode(\"overwrite\").saveAsTable(\"supply_chain\");\n", "labels": {"reads": [{"table": "attendees", "columns": null}], "writes": [{"table": "supply_chain", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ngo_funding SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"device_accessibility\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"specieswatertemp\")\n", "labels": {"reads": [{"table": "device_accessibility", "columns": null}], "writes": [{"table": "specieswatertemp", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stars SELECT area_sqkm, genrename, username, individual_middle_name FROM media_library WHERE area_sqkm > 187\"\n", "labels": {"reads": [{"table": "media_library", "columns": ["area_sqkm", "genrename", "username", "individual_middle_name"]}], "writes": [{"table": "stars", "columns": ["area_sqkm", "genrename", "username", "individual_middle_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"donations_insert_2\")\nsrc.write.insertInto(\"productsafety\", overwrite=True)\n", "labels": {"reads": [{"table": "donations_insert_2", "columns": null}], "writes": [{"table": "productsafety", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mental_health_clinics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"broadband_customers_global\")\n", "labels": {"reads": [{"table": "mental_health_clinics", "columns": null}], "writes": [{"table": "broadband_customers_global", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"invoice\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "invoice", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO menu_items SELECT coownerid, green_building_id FROM resource_extraction WHERE coownerid > 63\")\n", "labels": {"reads": [{"table": "resource_extraction", "columns": ["coownerid", "green_building_id"]}], "writes": [{"table": "menu_items", "columns": ["coownerid", "green_building_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO tourism_activities SELECT * FROM legacy\nspark.sql(\"INSERT INTO economic_diversification SELECT safety_record, studentname FROM mart_refunds_delta WHERE safety_record > 91\")\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": ["safety_record", "studentname"]}], "writes": [{"table": "economic_diversification", "columns": ["safety_record", "studentname"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 420;\nEOF\n", "labels": {"reads": [{"table": "climate_adaptation_re", "columns": ["destination", "algorithm_name", "number_of_hosts"]}], "writes": [{"table": "inmates", "columns": ["destination", "algorithm_name", "number_of_hosts"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO defense_spending_3 SELECT tree_species, prof_num FROM educators WHERE tree_species > 449\")\n", "labels": {"reads": [{"table": "educators", "columns": ["tree_species", "prof_num"]}], "writes": [{"table": "defense_spending_3", "columns": ["tree_species", "prof_num"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"cultural_events\");\ndf.write().mode(\"overwrite\").saveAsTable(\"match_result\");\n", "labels": {"reads": [{"table": "cultural_events", "columns": null}], "writes": [{"table": "match_result", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fossil_fuel_vehicles_japan\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles_japan", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.refunds_daily SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO functional_areas SELECT collection_id, staff_first_name, vehicle_flight_number, student FROM bikerental WHERE collection_id > 75\")\n", "labels": {"reads": [{"table": "bikerental", "columns": ["collection_id", "staff_first_name", "vehicle_flight_number", "student"]}], "writes": [{"table": "functional_areas", "columns": ["collection_id", "staff_first_name", "vehicle_flight_number", "student"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM smartcities\", conn)\ndf.to_sql(\"member_of\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "smartcities", "columns": null}], "writes": [{"table": "member_of", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ratings SELECT posted_at, time_id, is_sustainable, zip_code FROM nomination WHERE posted_at > 430\"\n", "labels": {"reads": [{"table": "nomination", "columns": ["posted_at", "time_id", "is_sustainable", "zip_code"]}], "writes": [{"table": "ratings", "columns": ["posted_at", "time_id", "is_sustainable", "zip_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"org_donation\").where(\"dt = current_date()\").writeTo(\"stg.refunds_daily\").append()\n", "labels": {"reads": [{"table": "org_donation", "columns": null}], "writes": [{"table": "stg.refunds_daily", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT organization, garment_material FROM autonomousdriving\", engine)\nimport logging\ndf.to_sql(\"subway\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "autonomousdriving", "columns": ["organization", "garment_material"]}], "writes": [{"table": "subway", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"accommodations\")\nsrc.write.insertInto(\"circular_supply_chain_products\", overwrite=True)\n", "labels": {"reads": [{"table": "accommodations", "columns": null}], "writes": [{"table": "circular_supply_chain_products", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT eco_certified, continent_id FROM brazil_projects\", engine)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"contract_negotiations_un\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "brazil_projects", "columns": ["eco_certified", "continent_id"]}], "writes": [{"table": "contract_negotiations_un", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tourist_attraction_features\", conn)\ndf.to_sql(\"landfill_capacity\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tourist_attraction_features", "columns": null}], "writes": [{"table": "landfill_capacity", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO accommodations SELECT playerid, conferenceid FROM ticket_sales WHERE playerid > 154\")\n", "labels": {"reads": [{"table": "ticket_sales", "columns": ["playerid", "conferenceid"]}], "writes": [{"table": "accommodations", "columns": ["playerid", "conferenceid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model characteristics depends on causes\ndbt run -s characteristics --vars 'source: causes'\n", "labels": {"reads": [{"table": "causes", "columns": null}], "writes": [{"table": "characteristics", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table marketing_budgets --target-dir /tmp/land\n", "labels": {"reads": [{"table": "marketing_budgets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM item\", conn)\ndf.to_sql(\"lessons\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "item", "columns": null}], "writes": [{"table": "lessons", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO dws.dws_coupon_use_full SELECT review_rating, device_name, contractor_name FROM patents WHERE review_rating > 89\");\n", "labels": {"reads": [{"table": "patents", "columns": ["review_rating", "device_name", "contractor_name"]}], "writes": [{"table": "dws.dws_coupon_use_full", "columns": ["review_rating", "device_name", "contractor_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO ods.ods_member_point_df SELECT dispensary_id, dispensaryid FROM spacecraft WHERE dispensary_id > 237\")\n", "labels": {"reads": [{"table": "spacecraft", "columns": ["dispensary_id", "dispensaryid"]}], "writes": [{"table": "ods.ods_member_point_df", "columns": ["dispensary_id", "dispensaryid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO community_centers SELECT * FROM legacy\ncur.execute(\"SELECT cb_year, date_moved_in FROM defensespending LIMIT 78\")\n", "labels": {"reads": [{"table": "defensespending", "columns": ["cb_year", "date_moved_in"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"doctors\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"electricvehicleadoption\")\n", "labels": {"reads": [{"table": "doctors", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bioprocess_engineering\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "bioprocess_engineering", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"volume\");\ndf.write().mode(\"overwrite\").saveAsTable(\"dw.dw_orders_hourly\");\n", "labels": {"reads": [{"table": "volume", "columns": null}], "writes": [{"table": "dw.dw_orders_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT served_subscribers, class FROM eco_diversification_investment\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"member_attendance\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "eco_diversification_investment", "columns": ["served_subscribers", "class"]}], "writes": [{"table": "member_attendance", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO ods_payments_delta SELECT experienceid, ethical_manufacturing, state, county FROM mart.risk_score_df WHERE experienceid > 206\"\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": ["experienceid", "ethical_manufacturing", "state", "county"]}], "writes": [{"table": "ods_payments_delta", "columns": ["experienceid", "ethical_manufacturing", "state", "county"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO route SELECT a.price_per_gram, b.attorneyid FROM stg.stg_campaigns_hourly a JOIN fans b ON a.friend = b.friend\"\n", "labels": {"reads": [{"table": "stg.stg_campaigns_hourly", "columns": null}, {"table": "fans", "columns": null}], "writes": [{"table": "route", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"investor_activities\");\ndf.write().mode(\"overwrite\").saveAsTable(\"creativeais\");\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": [{"table": "creativeais", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO apartment_facilities SELECT awayteamid, case_burden FROM music_streaming WHERE awayteamid > 184\");\n", "labels": {"reads": [{"table": "music_streaming", "columns": ["awayteamid", "case_burden"]}], "writes": [{"table": "apartment_facilities", "columns": ["awayteamid", "case_burden"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO spacecraft_components SELECT a.asset_acquired_date, b.facility_id FROM circuits a JOIN fairness_scores b ON a.profits_billion = b.profits_billion\"\n", "labels": {"reads": [{"table": "circuits", "columns": null}, {"table": "fairness_scores", "columns": null}], "writes": [{"table": "spacecraft_components", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"rd_expenditure\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "rd_expenditure", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 462;\nSQL\n", "labels": {"reads": [{"table": "dw.dw_orders_hourly", "columns": ["sport_id", "investor_name"]}, {"table": "route_fares", "columns": ["ship_date", "astronaut_name"]}], "writes": [{"table": "parties", "columns": ["ship_date", "astronaut_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"recycling_centers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"festivals\")\n", "labels": {"reads": [{"table": "recycling_centers", "columns": null}], "writes": [{"table": "festivals", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table safety_incidents_india --target-dir /tmp/land\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO technology_access SELECT topic, attendees FROM endowment WHERE topic > 449\");\n", "labels": {"reads": [{"table": "endowment", "columns": ["topic", "attendees"]}], "writes": [{"table": "technology_access", "columns": ["topic", "attendees"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model stg_coupon_use_hourly depends on organic_cosmetics\ndbt run --models stg_coupon_use_hourly --vars 'source: organic_cosmetics'\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": null}], "writes": [{"table": "stg_coupon_use_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO zip_codes SELECT marketing_region_descriptrion, lawyer_name, clinic_name FROM founders WHERE marketing_region_descriptrion > 59\"], check=True)\n", "labels": {"reads": [{"table": "founders", "columns": ["marketing_region_descriptrion", "lawyer_name", "clinic_name"]}], "writes": [{"table": "zip_codes", "columns": ["marketing_region_descriptrion", "lawyer_name", "clinic_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO accelerator_compatible_browser SELECT good_or_bad_customer, chw_id, goals FROM login_attempts WHERE good_or_bad_customer > 222\")\n", "labels": {"reads": [{"table": "login_attempts", "columns": ["good_or_bad_customer", "chw_id", "goals"]}], "writes": [{"table": "accelerator_compatible_browser", "columns": ["good_or_bad_customer", "chw_id", "goals"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"us_cities\")\npersist_to_target(df, \"bike_stations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "us_cities", "columns": null}], "writes": [{"table": "bike_stations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO public.developers SELECT card_number, is_electric, work_type FROM fault_log_parts WHERE card_number > 204\")\n", "labels": {"reads": [{"table": "fault_log_parts", "columns": ["card_number", "is_electric", "work_type"]}], "writes": [{"table": "public.developers", "columns": ["card_number", "is_electric", "work_type"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO gameattendance SELECT company_gender, size, strain_type FROM habitat WHERE company_gender > 218\")\n", "labels": {"reads": [{"table": "habitat", "columns": ["company_gender", "size", "strain_type"]}], "writes": [{"table": "gameattendance", "columns": ["company_gender", "size", "strain_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 378;\nSQL\n", "labels": {"reads": [{"table": "causes_insert_2", "columns": ["membership_amount", "famous_title"]}, {"table": "advisor", "columns": ["unitsperweek", "time_month"]}], "writes": [{"table": "procedures", "columns": ["unitsperweek", "time_month"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dwd_events_delta SELECT call_id, port_id, line_number FROM bioprocess_engineering WHERE call_id > 51\"\n", "labels": {"reads": [{"table": "bioprocess_engineering", "columns": ["call_id", "port_id", "line_number"]}], "writes": [{"table": "dwd_events_delta", "columns": ["call_id", "port_id", "line_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO weapons SELECT * FROM legacy\ncur.execute(\"SELECT order_shipping_charges, support_rate FROM climateresearch LIMIT 465\")\n", "labels": {"reads": [{"table": "climateresearch", "columns": ["order_shipping_charges", "support_rate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO singer SELECT attorney_last_name, drug_id, equipment_name, equipment_id FROM geologicalsurvey WHERE attorney_last_name > 382\")\n", "labels": {"reads": [{"table": "geologicalsurvey", "columns": ["attorney_last_name", "drug_id", "equipment_name", "equipment_id"]}], "writes": [{"table": "singer", "columns": ["attorney_last_name", "drug_id", "equipment_name", "equipment_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT chw_id, allergy FROM initiatives_3 LIMIT 378\")\nimport logging\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO user_activity SELECT dance_form, pd_id, pricepergram FROM government_transparency WHERE dance_form > 179\")\n", "labels": {"reads": [{"table": "initiatives_3", "columns": ["chw_id", "allergy"]}, {"table": "government_transparency", "columns": ["dance_form", "pd_id", "pricepergram"]}], "writes": [{"table": "user_activity", "columns": ["dance_form", "pd_id", "pricepergram"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM rating\", conn)\ndf.to_sql(\"urban_farms\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "rating", "columns": null}], "writes": [{"table": "urban_farms", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dwd.dwd_device_log_delta SELECT cause_name, impressions, patient_age FROM mentalhealthparityscores WHERE cause_name > 407\"\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": ["cause_name", "impressions", "patient_age"]}], "writes": [{"table": "dwd.dwd_device_log_delta", "columns": ["cause_name", "impressions", "patient_age"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"co2_emission_reduction\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "co2_emission_reduction", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO locations SELECT patient, initiative FROM spacecraft_temperatures WHERE patient > 51\");\n", "labels": {"reads": [{"table": "spacecraft_temperatures", "columns": ["patient", "initiative"]}], "writes": [{"table": "locations", "columns": ["patient", "initiative"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO bi.device_log SELECT all_home, ei_category, wage FROM missions WHERE all_home > 389\");\n", "labels": {"reads": [{"table": "missions", "columns": ["all_home", "ei_category", "wage"]}], "writes": [{"table": "bi.device_log", "columns": ["all_home", "ei_category", "wage"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO visits_restaurant SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"education_programs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "education_programs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO immunization SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ytterbium_supply depends on sales_quarterly\ndbt run --select ytterbium_supply --vars '{\"source_table\":\"sales_quarterly\"}'\n", "labels": {"reads": [{"table": "sales_quarterly", "columns": null}], "writes": [{"table": "ytterbium_supply", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"tech_workers_union\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO date SELECT eventtype, team, black, product_description FROM sfc_articles WHERE eventtype > 248\"\n", "labels": {"reads": [{"table": "sfc_articles", "columns": ["eventtype", "team", "black", "product_description"]}], "writes": [{"table": "date", "columns": ["eventtype", "team", "black", "product_description"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"retailers\")\nsrc.write.insertInto(\"mining_operations\", overwrite=True)\n", "labels": {"reads": [{"table": "retailers", "columns": null}], "writes": [{"table": "mining_operations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT book_title, process_id FROM ads.ads_campaigns_full LIMIT 119\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO rebounds SELECT specialty, machinery_id, warehouseid FROM project_timeline WHERE specialty > 383\")\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": ["book_title", "process_id"]}, {"table": "project_timeline", "columns": ["specialty", "machinery_id", "warehouseid"]}], "writes": [{"table": "rebounds", "columns": ["specialty", "machinery_id", "warehouseid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"staff_members\")\nsrc.write.insertInto(\"social_impact_bonds\", overwrite=True)\n", "labels": {"reads": [{"table": "staff_members", "columns": null}], "writes": [{"table": "social_impact_bonds", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO item_prices SELECT date_id, stu_dob, menuitem FROM disaster_response_donations WHERE date_id > 401\");\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": ["date_id", "stu_dob", "menuitem"]}], "writes": [{"table": "item_prices", "columns": ["date_id", "stu_dob", "menuitem"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM exhibition_artworks\"\n", "labels": {"reads": [{"table": "exhibition_artworks", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mammals\").toPandas()\ndf[[\"class_president_vote\", \"indigenous\"]].to_sql(\"mart.mart_users_di\", engine, index=False)\n", "labels": {"reads": [{"table": "mammals", "columns": null}], "writes": [{"table": "mart.mart_users_di", "columns": ["class_president_vote", "indigenous"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO eventlocations SELECT subject_name, precipitation, product_subcategory, review_rating FROM assets WHERE subject_name > 180\"\n", "labels": {"reads": [{"table": "assets", "columns": ["subject_name", "precipitation", "product_subcategory", "review_rating"]}], "writes": [{"table": "eventlocations", "columns": ["subject_name", "precipitation", "product_subcategory", "review_rating"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO transport SELECT driver_id, total_horses FROM sustainable_projects WHERE driver_id > 229\")\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["driver_id", "total_horses"]}], "writes": [{"table": "transport", "columns": ["driver_id", "total_horses"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"adaptation_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"band\")\n", "labels": {"reads": [{"table": "adaptation_projects", "columns": null}], "writes": [{"table": "band", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"climber\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "climber", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO auto_shows SELECT participated_in_open_pedagogy, date_claim_made, coownerid FROM ratings WHERE participated_in_open_pedagogy > 439\"\n", "labels": {"reads": [{"table": "ratings", "columns": ["participated_in_open_pedagogy", "date_claim_made", "coownerid"]}], "writes": [{"table": "auto_shows", "columns": ["participated_in_open_pedagogy", "date_claim_made", "coownerid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"community_engagement\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "community_engagement", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nsql = \"INSERT INTO site SELECT a.shipping_agent_code, b.insurancetype FROM refugee_support a JOIN recyclingrates b ON a.donortype = b.donortype\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "refugee_support", "columns": null}, {"table": "recyclingrates", "columns": null}], "writes": [{"table": "site", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO machinery (billing_country, assets) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "machinery", "columns": ["billing_country", "assets"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO supplier_addresses SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO production_sites SELECT strain_type, instrument FROM urbanagricrop WHERE strain_type > 492\")\n", "labels": {"reads": [{"table": "urbanagricrop", "columns": ["strain_type", "instrument"]}], "writes": [{"table": "production_sites", "columns": ["strain_type", "instrument"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO festivals (manager_name, stat_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "festivals", "columns": ["manager_name", "stat_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table equipmentsales --columns cust_id,dname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "equipmentsales", "columns": ["cust_id", "dname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dp_articles\").where(\"dt = current_date()\").writeTo(\"workplaces\").append()\n", "labels": {"reads": [{"table": "dp_articles", "columns": null}], "writes": [{"table": "workplaces", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO climate_projects SELECT bikes_available, requestdate, match_id FROM ai_ethics_policies WHERE bikes_available > 215\"\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": ["bikes_available", "requestdate", "match_id"]}], "writes": [{"table": "climate_projects", "columns": ["bikes_available", "requestdate", "match_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO coffee_prices SELECT date_of_attendance, premise_details, mental_health_resource_access, vendorname FROM market_trends WHERE date_of_attendance > 57\")\n", "labels": {"reads": [{"table": "market_trends", "columns": ["date_of_attendance", "premise_details", "mental_health_resource_access", "vendorname"]}], "writes": [{"table": "coffee_prices", "columns": ["date_of_attendance", "premise_details", "mental_health_resource_access", "vendorname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 393;\nSQL\n", "labels": {"reads": [{"table": "rural_resources", "columns": ["official_native_language", "menu_type"]}, {"table": "athletes_performance", "columns": ["ratingdate", "investment_date", "artifacttype"]}], "writes": [{"table": "invoices", "columns": ["ratingdate", "investment_date", "artifacttype"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO ads.ads_users_hourly SELECT attendance, updatedate FROM behavior_incident WHERE attendance > 439\")\n", "labels": {"reads": [{"table": "behavior_incident", "columns": ["attendance", "updatedate"]}], "writes": [{"table": "ads.ads_users_hourly", "columns": ["attendance", "updatedate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO player_coach SELECT * FROM legacy\nspark.sql(\"INSERT INTO carbon_offset_south_america SELECT personal_name, customer_last_name FROM dancefunding WHERE personal_name > 135\")\n", "labels": {"reads": [{"table": "dancefunding", "columns": ["personal_name", "customer_last_name"]}], "writes": [{"table": "carbon_offset_south_america", "columns": ["personal_name", "customer_last_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws_clicks_di (ai_model, runtime) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws_clicks_di", "columns": ["ai_model", "runtime"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"streams\")\nsrc.write.insertInto(\"container_ships\", overwrite=True)\n", "labels": {"reads": [{"table": "streams", "columns": null}], "writes": [{"table": "container_ships", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"customer_transactions\")\nsrc.write.insertInto(\"sanctuaryanimals\", overwrite=True)\n", "labels": {"reads": [{"table": "customer_transactions", "columns": null}], "writes": [{"table": "sanctuaryanimals", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT funding_round_id, status FROM customer_size_diversity\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"waterconservationbudget\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": ["funding_round_id", "status"]}], "writes": [{"table": "waterconservationbudget", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 367;\nSQL\n", "labels": {"reads": [{"table": "geological_survey", "columns": ["attendee_age", "call_count"]}, {"table": "vesselarrivals", "columns": ["shipment_tracking_number", "resolutiondate", "workeridentity", "service_type_code"]}], "writes": [{"table": "canals", "columns": ["shipment_tracking_number", "resolutiondate", "workeridentity", "service_type_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"inclusivehousing.affordablehousing\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "inclusivehousing.affordablehousing", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO laborstatistics SELECT product_category_code, initiative, loan_amount FROM bus_routes WHERE product_category_code > 120\")\n", "labels": {"reads": [{"table": "bus_routes", "columns": ["product_category_code", "initiative", "loan_amount"]}], "writes": [{"table": "laborstatistics", "columns": ["product_category_code", "initiative", "loan_amount"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT measurement_date, certification_name FROM seeds\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"vessels_2\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "seeds", "columns": ["measurement_date", "certification_name"]}], "writes": [{"table": "vessels_2", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_input(ctx, \"dwd_risk_score_hourly\")\npush_to_target(df, \"renewable_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd_risk_score_hourly", "columns": null}], "writes": [{"table": "renewable_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO cosmetic_sales SELECT a.f_id, b.program FROM mentalhealthprovider a JOIN user_genre b ON a.water_usage = b.water_usage\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": null}, {"table": "user_genre", "columns": null}], "writes": [{"table": "cosmetic_sales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT emission_date, trial_name FROM grad_students LIMIT 457\")\nrows = cur.fetchall()\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "grad_students", "columns": ["emission_date", "trial_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"atlantic_ocean_fish\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "atlantic_ocean_fish", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO plants SELECT rig_name, transaction_amount, founders FROM habitat WHERE rig_name > 403\"\n", "labels": {"reads": [{"table": "habitat", "columns": ["rig_name", "transaction_amount", "founders"]}], "writes": [{"table": "plants", "columns": ["rig_name", "transaction_amount", "founders"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO spacecraft_manufacturers SELECT a.preference_rating, b.opponent_id FROM ref_budget_codes a JOIN mart.mart_coupon_use_full b ON a.healthcareid = b.healthcareid\"\n", "labels": {"reads": [{"table": "ref_budget_codes", "columns": null}, {"table": "mart.mart_coupon_use_full", "columns": null}], "writes": [{"table": "spacecraft_manufacturers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT region_code, researcher FROM member_attendance\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"rural_infrastructure\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "member_attendance", "columns": ["region_code", "researcher"]}], "writes": [{"table": "rural_infrastructure", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO safetyincidents (practice, sensor_reading) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "safetyincidents", "columns": ["practice", "sensor_reading"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO pollutionincidents SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO researcher SELECT product_type, sqft, instid FROM sourcing WHERE product_type > 308\"\n", "labels": {"reads": [{"table": "sourcing", "columns": ["product_type", "sqft", "instid"]}], "writes": [{"table": "researcher", "columns": ["product_type", "sqft", "instid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"properties\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"community_education_programs\")\n", "labels": {"reads": [{"table": "properties", "columns": null}], "writes": [{"table": "community_education_programs", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO cultural_competency SELECT a.sale_amount, b.farmid FROM multimodalhubs a JOIN bi_inventory_hourly b ON a.transportation_method = b.transportation_method\"\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": null}, {"table": "bi_inventory_hourly", "columns": null}], "writes": [{"table": "cultural_competency", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO disaster_zones SELECT artifact_weight, pallet_id, vendor_state FROM mentalhealthprofessional WHERE artifact_weight > 480\");\n", "labels": {"reads": [{"table": "mentalhealthprofessional", "columns": ["artifact_weight", "pallet_id", "vendor_state"]}], "writes": [{"table": "disaster_zones", "columns": ["artifact_weight", "pallet_id", "vendor_state"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 480;\nEOF\n", "labels": {"reads": [{"table": "contract_states", "columns": ["production_budget", "complaint_type_code", "sustainability_id"]}], "writes": [{"table": "parties", "columns": ["production_budget", "complaint_type_code", "sustainability_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO experience SELECT a.purchaseid, b.authors FROM opendatainitiatives a JOIN participants b ON a.employee_name = b.employee_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "opendatainitiatives", "columns": null}, {"table": "participants", "columns": null}], "writes": [{"table": "experience", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bioprocess.engineering_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"courses\")\n", "labels": {"reads": [{"table": "bioprocess.engineering_projects", "columns": null}], "writes": [{"table": "courses", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ma_inspections\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"sitem\")\n", "labels": {"reads": [{"table": "ma_inspections", "columns": null}], "writes": [{"table": "sitem", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO safetyincidents SELECT unique_founders, asset_disposed_date, party_phone FROM europium_exports WHERE unique_founders > 203\"], check=True)\n", "labels": {"reads": [{"table": "europium_exports", "columns": ["unique_founders", "asset_disposed_date", "party_phone"]}], "writes": [{"table": "safetyincidents", "columns": ["unique_founders", "asset_disposed_date", "party_phone"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO biomes SELECT * FROM legacy\nspark.sql(\"INSERT INTO dws.cart_item_di SELECT has_parabens, call_count, policyid FROM police_stations WHERE has_parabens > 127\")\n", "labels": {"reads": [{"table": "police_stations", "columns": ["has_parabens", "call_count", "policyid"]}], "writes": [{"table": "dws.cart_item_di", "columns": ["has_parabens", "call_count", "policyid"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sustainable_building SELECT license_type, uk_vat_number FROM product_review WHERE license_type > 416\"\n", "labels": {"reads": [{"table": "product_review", "columns": ["license_type", "uk_vat_number"]}], "writes": [{"table": "sustainable_building", "columns": ["license_type", "uk_vat_number"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"school_details\")\nupsert_to_store(df, \"ref_incident_type\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "school_details", "columns": null}], "writes": [{"table": "ref_incident_type", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ref_incident_type SELECT unavailable, stu_hrs, no_of_customers, donortype FROM mexico_regions WHERE unavailable > 216\"\n", "labels": {"reads": [{"table": "mexico_regions", "columns": ["unavailable", "stu_hrs", "no_of_customers", "donortype"]}], "writes": [{"table": "ref_incident_type", "columns": ["unavailable", "stu_hrs", "no_of_customers", "donortype"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM artsandcrafts\", conn)\ndf.to_sql(\"engineer_skills\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "artsandcrafts", "columns": null}], "writes": [{"table": "engineer_skills", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nsql = \"INSERT INTO us_platforms SELECT a.hoursperweek, b.amount_outstanding FROM ods.ods_risk_score_full a JOIN office_locations b ON a.wildlife_type_id = b.wildlife_type_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.ods_risk_score_full", "columns": null}, {"table": "office_locations", "columns": null}], "writes": [{"table": "us_platforms", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table candidates --columns contract_number,outcome_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "candidates", "columns": ["contract_number", "outcome_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_risk_score_hourly SELECT * FROM legacy\ncur.execute(\"SELECT competition, ll_hours FROM material LIMIT 337\")\n", "labels": {"reads": [{"table": "material", "columns": ["competition", "ll_hours"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT observation_id, production_id FROM autonomousvehicles\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"clothingitems\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "autonomousvehicles", "columns": ["observation_id", "production_id"]}], "writes": [{"table": "clothingitems", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO country_renewable_energy SELECT equipment_name, hearingdate, lastclaimdate, living_wage FROM wellbeing_program_participants WHERE equipment_name > 114\");\n", "labels": {"reads": [{"table": "wellbeing_program_participants", "columns": ["equipment_name", "hearingdate", "lastclaimdate", "living_wage"]}], "writes": [{"table": "country_renewable_energy", "columns": ["equipment_name", "hearingdate", "lastclaimdate", "living_wage"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO request SELECT phase, rental_date, creationyear, anomaly FROM wind_farms WHERE phase > 185\"\n", "labels": {"reads": [{"table": "wind_farms", "columns": ["phase", "rental_date", "creationyear", "anomaly"]}], "writes": [{"table": "request", "columns": ["phase", "rental_date", "creationyear", "anomaly"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fair_wages\", conn)\ndf.to_sql(\"carbon_offsets.carbon_offsets\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fair_wages", "columns": null}], "writes": [{"table": "carbon_offsets.carbon_offsets", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model state_usage depends on behavior_incident\ndbt build -s state_usage --vars '{\"src\":\"behavior_incident\"}'\n", "labels": {"reads": [{"table": "behavior_incident", "columns": null}], "writes": [{"table": "state_usage", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"dws_clicks_di\")\nwrite_to_output(df, \"vr_adopters\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws_clicks_di", "columns": null}], "writes": [{"table": "vr_adopters", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO trainers SELECT strain_type, expertise, strategy_id, prereq_id FROM soil_moisture WHERE strain_type > 399\"\n", "labels": {"reads": [{"table": "soil_moisture", "columns": ["strain_type", "expertise", "strategy_id", "prereq_id"]}], "writes": [{"table": "trainers", "columns": ["strain_type", "expertise", "strategy_id", "prereq_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table athlete_stats --target-dir /tmp/land\n", "labels": {"reads": [{"table": "athlete_stats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 226;\nSQL\n", "labels": {"reads": [{"table": "textile_waste", "columns": ["dock_id", "material_name"]}, {"table": "ads_coupon_use_full", "columns": ["milliseconds", "diet"]}], "writes": [{"table": "support", "columns": ["milliseconds", "diet"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM athletes_performance\"\n", "labels": {"reads": [{"table": "athletes_performance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"schools\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"chemical_processes\")\n", "labels": {"reads": [{"table": "schools", "columns": null}], "writes": [{"table": "chemical_processes", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO culturalevents SELECT professionalid, competition_type, num_libraries, aid FROM item_inventory WHERE professionalid > 435\");\n", "labels": {"reads": [{"table": "item_inventory", "columns": ["professionalid", "competition_type", "num_libraries", "aid"]}], "writes": [{"table": "culturalevents", "columns": ["professionalid", "competition_type", "num_libraries", "aid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"union_members\")\npush_to_warehouse(df, \"educators\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "union_members", "columns": null}], "writes": [{"table": "educators", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"tech_for_social_good\");\ndf.write().mode(\"overwrite\").saveAsTable(\"talent_acquisition\");\n", "labels": {"reads": [{"table": "tech_for_social_good", "columns": null}], "writes": [{"table": "talent_acquisition", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO captain SELECT * FROM legacy\ncur.execute(\"SELECT installed_date, energy_production FROM restaurant_revenue LIMIT 47\")\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": ["installed_date", "energy_production"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table financial_capability_program --columns donor_state,location --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "financial_capability_program", "columns": ["donor_state", "location"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO job_postings SELECT profits_in_billion, num_libraries, vessel_name FROM pharmasales WHERE profits_in_billion > 245\"\n", "labels": {"reads": [{"table": "pharmasales", "columns": ["profits_in_billion", "num_libraries", "vessel_name"]}], "writes": [{"table": "job_postings", "columns": ["profits_in_billion", "num_libraries", "vessel_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ethicalaibudget\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"electricvehicleadoption\")\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"russia_nato_diplomacy\");\ndf.write().mode(\"overwrite\").saveAsTable(\"cuisine\");\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": null}], "writes": [{"table": "cuisine", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO expensive_space_missions SELECT a.event_type, b.policy_holder_id FROM commercialbuildings a JOIN genderdistribution b ON a.hourlyrate = b.hourlyrate\"\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": null}, {"table": "genderdistribution", "columns": null}], "writes": [{"table": "expensive_space_missions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO student_program_mapping SELECT balance, stuid, artworkyear, document_structure_description FROM match WHERE balance > 349\")\n", "labels": {"reads": [{"table": "match", "columns": ["balance", "stuid", "artworkyear", "document_structure_description"]}], "writes": [{"table": "student_program_mapping", "columns": ["balance", "stuid", "artworkyear", "document_structure_description"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO drills SELECT a.artifactname, b.museumid FROM latam_schema.education_budget a JOIN defenseprojects b ON a.film_id = b.film_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": null}, {"table": "defenseprojects", "columns": null}], "writes": [{"table": "drills", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM port\", conn)\ndf.to_sql(\"biosensors.readings\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "port", "columns": null}], "writes": [{"table": "biosensors.readings", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 18;\nEOF\n", "labels": {"reads": [{"table": "recyclednylongarments", "columns": ["providerid", "frameworkcountry"]}], "writes": [{"table": "playergamehistory", "columns": ["providerid", "frameworkcountry"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"electric_vehicle_stats\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"airportdata\")\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": null}], "writes": [{"table": "airportdata", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO healthcare_centers SELECT common_name, test_result, potency, impressions FROM web_client_accelerator WHERE common_name > 222\")\n", "labels": {"reads": [{"table": "web_client_accelerator", "columns": ["common_name", "test_result", "potency", "impressions"]}], "writes": [{"table": "healthcare_centers", "columns": ["common_name", "test_result", "potency", "impressions"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"budget_allocations\").toPandas()\ndf[[\"surname\", \"programarea\"]].to_sql(\"latam_schema.education_budget\", engine, index=False)\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "latam_schema.education_budget", "columns": ["surname", "programarea"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO classrooms SELECT classid, attack_date, date_of_transaction, apt_type_code FROM stg.refunds_hourly WHERE classid > 218\")\n", "labels": {"reads": [{"table": "stg.refunds_hourly", "columns": ["classid", "attack_date", "date_of_transaction", "apt_type_code"]}], "writes": [{"table": "classrooms", "columns": ["classid", "attack_date", "date_of_transaction", "apt_type_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"agencies\")\npush_to_target(df, \"customer_policies\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "agencies", "columns": null}], "writes": [{"table": "customer_policies", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO acceptance (staff_details, name_last) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "acceptance", "columns": ["staff_details", "name_last"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table greenbuildings --columns access_count,attendanceid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "greenbuildings", "columns": ["access_count", "attendanceid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO underwater_cables SELECT service_details, form_id, pid FROM ocean_health_monitor WHERE service_details > 346\"\n", "labels": {"reads": [{"table": "ocean_health_monitor", "columns": ["service_details", "form_id", "pid"]}], "writes": [{"table": "underwater_cables", "columns": ["service_details", "form_id", "pid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.inventory_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"wastewater_treatment_plants\")\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": null}], "writes": [{"table": "wastewater_treatment_plants", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table support_programs --columns habitat_id,insurancetype --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "support_programs", "columns": ["habitat_id", "insurancetype"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 141;\nSQL\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": ["trader_id", "work_type"]}, {"table": "port", "columns": ["alid", "clinic_id"]}], "writes": [{"table": "pilot", "columns": ["alid", "clinic_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cultural_competency\").toPandas()\ndf[[\"artist_name\", \"number_city_affected\"]].to_sql(\"supplier_ethics\", engine, index=False)\n", "labels": {"reads": [{"table": "cultural_competency", "columns": null}], "writes": [{"table": "supplier_ethics", "columns": ["artist_name", "number_city_affected"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO transportation SELECT sale_quantity, equipment_id, book_title FROM player_award WHERE sale_quantity > 439\"\n", "labels": {"reads": [{"table": "player_award", "columns": ["sale_quantity", "equipment_id", "book_title"]}], "writes": [{"table": "transportation", "columns": ["sale_quantity", "equipment_id", "book_title"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws.payments_delta SELECT follows_ethical_practices, population FROM ads_vendors_hourly WHERE follows_ethical_practices > 350\"], check=True)\n", "labels": {"reads": [{"table": "ads_vendors_hourly", "columns": ["follows_ethical_practices", "population"]}], "writes": [{"table": "dws.payments_delta", "columns": ["follows_ethical_practices", "population"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO researchers SELECT emp_id, program_id, collection_id FROM researchgrants WHERE emp_id > 34\"\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["emp_id", "program_id", "collection_id"]}], "writes": [{"table": "researchers", "columns": ["emp_id", "program_id", "collection_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"submission\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "submission", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model exit_strategies depends on mine_workforce\ndbt run --select exit_strategies --vars '{\"src\":\"mine_workforce\"}'\n", "labels": {"reads": [{"table": "mine_workforce", "columns": null}], "writes": [{"table": "exit_strategies", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO tb_reports SELECT * FROM legacy\nspark.sql(\"INSERT INTO climate_finance_re SELECT has_disability, primary_conference FROM audience_demographics WHERE has_disability > 447\")\n", "labels": {"reads": [{"table": "audience_demographics", "columns": ["has_disability", "primary_conference"]}], "writes": [{"table": "climate_finance_re", "columns": ["has_disability", "primary_conference"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table game_scores --columns roomid,working_horses --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "game_scores", "columns": ["roomid", "working_horses"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO minor_in SELECT session_name, song_id FROM procedures WHERE session_name > 213\")\n", "labels": {"reads": [{"table": "procedures", "columns": ["session_name", "song_id"]}], "writes": [{"table": "minor_in", "columns": ["session_name", "song_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO item_inventory SELECT * FROM legacy\ncur.execute(\"SELECT farm_id, winning_pilot FROM vesselarrivals LIMIT 491\")\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": ["farm_id", "winning_pilot"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO climate_communication_projects (transaction_value, price_in_dollar) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "climate_communication_projects", "columns": ["transaction_value", "price_in_dollar"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donation_id, secretary_vote FROM habitat LIMIT 382\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "habitat", "columns": ["donation_id", "secretary_vote"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.virtual_tour_engagement_time > 279).all()\n# src table: genre_songs\nengine.execute(\"INSERT INTO military_spending SELECT * FROM genre_songs\")\n", "labels": {"reads": [{"table": "genre_songs", "columns": null}], "writes": [{"table": "military_spending", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fields SELECT watch_time, claim_type FROM pipelines WHERE watch_time > 116\"\n", "labels": {"reads": [{"table": "pipelines", "columns": ["watch_time", "claim_type"]}], "writes": [{"table": "fields", "columns": ["watch_time", "claim_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO investment_strategies SELECT tasktype, trip_date, co_id FROM dws.dws_cart_item_daily WHERE tasktype > 314\"], check=True)\n", "labels": {"reads": [{"table": "dws.dws_cart_item_daily", "columns": ["tasktype", "trip_date", "co_id"]}], "writes": [{"table": "investment_strategies", "columns": ["tasktype", "trip_date", "co_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO traffic_violations SELECT a.competition_type, b.interest_group FROM traffic_accidents a JOIN recyclers b ON a.technician_id = b.technician_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "traffic_accidents", "columns": null}, {"table": "recyclers", "columns": null}], "writes": [{"table": "traffic_violations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dwd.dwd_payments SELECT production_usage, animal_id, year_built FROM product_reviews WHERE production_usage > 496\"\n", "labels": {"reads": [{"table": "product_reviews", "columns": ["production_usage", "animal_id", "year_built"]}], "writes": [{"table": "dwd.dwd_payments", "columns": ["production_usage", "animal_id", "year_built"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"recycling_rates_oceania\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "recycling_rates_oceania", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"co2emissions\").toPandas()\ndf[[\"src_apid\", \"courses\"]].to_sql(\"bi.bi_risk_score_full\", engine, index=False)\n", "labels": {"reads": [{"table": "co2emissions", "columns": null}], "writes": [{"table": "bi.bi_risk_score_full", "columns": ["src_apid", "courses"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"socialimpactinvestments\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO tech_volunteers SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO train_lines SELECT build_year, train_id, exhibition_id FROM nba_games WHERE build_year > 19\")\n", "labels": {"reads": [{"table": "nba_games", "columns": ["build_year", "train_id", "exhibition_id"]}], "writes": [{"table": "train_lines", "columns": ["build_year", "train_id", "exhibition_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tasks SELECT species_name, risk_score, fault_status FROM battery_projects WHERE species_name > 343\"], check=True)\n", "labels": {"reads": [{"table": "battery_projects", "columns": ["species_name", "risk_score", "fault_status"]}], "writes": [{"table": "tasks", "columns": ["species_name", "risk_score", "fault_status"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO hockey_players SELECT * FROM legacy\nspark.sql(\"INSERT INTO community_events SELECT port_code, request FROM intelligenceoperations WHERE port_code > 25\")\n", "labels": {"reads": [{"table": "intelligenceoperations", "columns": ["port_code", "request"]}], "writes": [{"table": "community_events", "columns": ["port_code", "request"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 35;\nSQL\n", "labels": {"reads": [{"table": "organic_farms", "columns": ["hours_billed", "mental_health_score"]}, {"table": "workplaces", "columns": ["machinery_id", "status", "authid"]}], "writes": [{"table": "militarypersonnel", "columns": ["machinery_id", "status", "authid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.users_df SELECT equipment_id, min_salary, has_aloe_vera, researcher FROM experience WHERE equipment_id > 117\"\n", "labels": {"reads": [{"table": "experience", "columns": ["equipment_id", "min_salary", "has_aloe_vera", "researcher"]}], "writes": [{"table": "bi.users_df", "columns": ["equipment_id", "min_salary", "has_aloe_vera", "researcher"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO cerium_production SELECT num_beds, passenger_count, payment_date FROM ocean_floor_depth WHERE num_beds > 11\"\n", "labels": {"reads": [{"table": "ocean_floor_depth", "columns": ["num_beds", "passenger_count", "payment_date"]}], "writes": [{"table": "cerium_production", "columns": ["num_beds", "passenger_count", "payment_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vehicle_registrations SELECT a.passenger_id, b.date_of_transaction FROM agri_innov a JOIN dwd.dwd_coupon_use_df b ON a.centername = b.centername\"\n", "labels": {"reads": [{"table": "agri_innov", "columns": null}, {"table": "dwd.dwd_coupon_use_df", "columns": null}], "writes": [{"table": "vehicle_registrations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM marine_species_observations\"\n", "labels": {"reads": [{"table": "marine_species_observations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.bi_campaigns_delta SELECT training_name, sculpture_name, investmentid FROM environmentalimpact WHERE training_name > 184\"\n", "labels": {"reads": [{"table": "environmentalimpact", "columns": ["training_name", "sculpture_name", "investmentid"]}], "writes": [{"table": "bi.bi_campaigns_delta", "columns": ["training_name", "sculpture_name", "investmentid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO us_military_personnel SELECT * FROM legacy\nspark.sql(\"INSERT INTO bi.device_log SELECT num_stops, date_joined_staff, movie, crime_date FROM autonomous_testing WHERE num_stops > 23\")\n", "labels": {"reads": [{"table": "autonomous_testing", "columns": ["num_stops", "date_joined_staff", "movie", "crime_date"]}], "writes": [{"table": "bi.device_log", "columns": ["num_stops", "date_joined_staff", "movie", "crime_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO riskassessments SELECT allergy, is_safe, funding_source, catalog_level_number FROM agriculturalinnovations WHERE allergy > 20\"\n", "labels": {"reads": [{"table": "agriculturalinnovations", "columns": ["allergy", "is_safe", "funding_source", "catalog_level_number"]}], "writes": [{"table": "riskassessments", "columns": ["allergy", "is_safe", "funding_source", "catalog_level_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO dws.dws_coupon_use_di SELECT port_name, export_country FROM coal WHERE port_name > 439\");\n", "labels": {"reads": [{"table": "coal", "columns": ["port_name", "export_country"]}], "writes": [{"table": "dws.dws_coupon_use_di", "columns": ["port_name", "export_country"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO roller_coaster SELECT camera_lens_id, patient_age FROM ingredient_sourcing WHERE camera_lens_id > 311\"\n", "labels": {"reads": [{"table": "ingredient_sourcing", "columns": ["camera_lens_id", "patient_age"]}], "writes": [{"table": "roller_coaster", "columns": ["camera_lens_id", "patient_age"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO bi.bi_payments SELECT a.contract_start_date, b.firstdonationdate FROM exhibition_visits a JOIN stg.stg_exposure_di b ON a.energy_generated = b.energy_generated\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": null}, {"table": "stg.stg_exposure_di", "columns": null}], "writes": [{"table": "bi.bi_payments", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO vessel_registry SELECT sale_revenue, tour_name, publication_id FROM city_department WHERE sale_revenue > 51\")\n", "labels": {"reads": [{"table": "city_department", "columns": ["sale_revenue", "tour_name", "publication_id"]}], "writes": [{"table": "vessel_registry", "columns": ["sale_revenue", "tour_name", "publication_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO music SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO exhibition_visitors (enzyme_id, financially_capable) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "exhibition_visitors", "columns": ["enzyme_id", "financially_capable"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO school_details SELECT cruelty_free, outcome, volunteerhourid FROM genetics.experiments WHERE cruelty_free > 73\")\n", "labels": {"reads": [{"table": "genetics.experiments", "columns": ["cruelty_free", "outcome", "volunteerhourid"]}], "writes": [{"table": "school_details", "columns": ["cruelty_free", "outcome", "volunteerhourid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO africa_schema.african_mines SELECT branch, prof_office FROM bi_device_log_daily WHERE branch > 49\"\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": ["branch", "prof_office"]}], "writes": [{"table": "africa_schema.african_mines", "columns": ["branch", "prof_office"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO carbon_offset_south_america SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dw_users_full SELECT * FROM legacy\ncur.execute(\"SELECT branch, acc_percent FROM players LIMIT 499\")\n", "labels": {"reads": [{"table": "players", "columns": ["branch", "acc_percent"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"satellitedata\");\ndf.write().mode(\"overwrite\").saveAsTable(\"bi.bi_vendors_di\");\n", "labels": {"reads": [{"table": "satellitedata", "columns": null}], "writes": [{"table": "bi.bi_vendors_di", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sustainabilityratings SELECT a.closuredate, b.certification_id FROM autonomous_testing a JOIN shariah_compliant_loans b ON a.num_projects = b.num_projects\"\n", "labels": {"reads": [{"table": "autonomous_testing", "columns": null}, {"table": "shariah_compliant_loans", "columns": null}], "writes": [{"table": "sustainabilityratings", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO decentralized_applications SELECT workshop_name, season FROM public.forest_stats WHERE workshop_name > 121\")\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": ["workshop_name", "season"]}], "writes": [{"table": "decentralized_applications", "columns": ["workshop_name", "season"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.work_type > 444).all()\n# src table: workersalaries\nengine.execute(\"INSERT INTO city_waste_generation SELECT * FROM workersalaries\")\n", "labels": {"reads": [{"table": "workersalaries", "columns": null}], "writes": [{"table": "city_waste_generation", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.booking_date > 202).all()\n# src table: parties\nengine.execute(\"INSERT INTO ads.ads_vendors_hourly SELECT * FROM parties\")\n", "labels": {"reads": [{"table": "parties", "columns": null}], "writes": [{"table": "ads.ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"movie_ratings\")\nsrc.write.insertInto(\"ads.ads_risk_score_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "movie_ratings", "columns": null}], "writes": [{"table": "ads.ads_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"initiative_types\");\ndf.write().mode(\"overwrite\").saveAsTable(\"user_stats\");\n", "labels": {"reads": [{"table": "initiative_types", "columns": null}], "writes": [{"table": "user_stats", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.countryid > 498).all()\n# src table: ads.ads_inventory_df\nengine.execute(\"INSERT INTO stg.stg_events_hourly SELECT * FROM ads.ads_inventory_df\")\n", "labels": {"reads": [{"table": "ads.ads_inventory_df", "columns": null}], "writes": [{"table": "stg.stg_events_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO dwd.exposure_hourly SELECT singer_id, time_stamp, deaths, budgetid FROM impact_investments WHERE singer_id > 54\")\n", "labels": {"reads": [{"table": "impact_investments", "columns": ["singer_id", "time_stamp", "deaths", "budgetid"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": ["singer_id", "time_stamp", "deaths", "budgetid"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_clicks_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"marine_life_data\")\n", "labels": {"reads": [{"table": "ods_clicks_df", "columns": null}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stay\"\n", "labels": {"reads": [{"table": "stay", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"ods.ods_payments_full\")\npersist_to_output(df, \"rural_hospitals\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods.ods_payments_full", "columns": null}], "writes": [{"table": "rural_hospitals", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO flight_emissions SELECT order_item_status, provider_parity_score, developer, num_investments FROM philadelphia_police_emergencies WHERE order_item_status > 211\"\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": ["order_item_status", "provider_parity_score", "developer", "num_investments"]}], "writes": [{"table": "flight_emissions", "columns": ["order_item_status", "provider_parity_score", "developer", "num_investments"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO communitycourtcases SELECT contributorname, permit_number, oct, school_colors FROM incarcerated WHERE contributorname > 27\"\n", "labels": {"reads": [{"table": "incarcerated", "columns": ["contributorname", "permit_number", "oct", "school_colors"]}], "writes": [{"table": "communitycourtcases", "columns": ["contributorname", "permit_number", "oct", "school_colors"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"boston_emergency_response\")\nsrc.write.insertInto(\"service_budget\", overwrite=True)\n", "labels": {"reads": [{"table": "boston_emergency_response", "columns": null}], "writes": [{"table": "service_budget", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bridgeconstruction\", conn)\ndf.to_sql(\"mining_operations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bridgeconstruction", "columns": null}], "writes": [{"table": "mining_operations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM artwork_styles\"\n", "labels": {"reads": [{"table": "artwork_styles", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table research --target-dir /tmp/land\n", "labels": {"reads": [{"table": "research", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cars --columns don_name,resolution --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cars", "columns": ["don_name", "resolution"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO farm SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT objectnumber, register_year FROM school_roster LIMIT 19\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "school_roster", "columns": ["objectnumber", "register_year"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_campaigns_daily SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"carbon_offset_initiatives\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"decentralized_applications\")\n", "labels": {"reads": [{"table": "carbon_offset_initiatives", "columns": null}], "writes": [{"table": "decentralized_applications", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO roads SELECT unsure_rate, fare_amount, violation_type FROM ods.ods_users_di WHERE unsure_rate > 375\"\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": ["unsure_rate", "fare_amount", "violation_type"]}], "writes": [{"table": "roads", "columns": ["unsure_rate", "fare_amount", "violation_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO territory.human_rights_data SELECT archaeologistid, members FROM characteristics WHERE archaeologistid > 120\");\n", "labels": {"reads": [{"table": "characteristics", "columns": ["archaeologistid", "members"]}], "writes": [{"table": "territory.human_rights_data", "columns": ["archaeologistid", "members"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO labor_productivity SELECT program_date, donorid, player_api_id, total FROM dw.users_hourly WHERE program_date > 108\"\n", "labels": {"reads": [{"table": "dw.users_hourly", "columns": ["program_date", "donorid", "player_api_id", "total"]}], "writes": [{"table": "labor_productivity", "columns": ["program_date", "donorid", "player_api_id", "total"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.uk_vat_number > 82).all()\n# src table: interventions\nengine.execute(\"INSERT INTO exhibition_record SELECT * FROM interventions\")\n", "labels": {"reads": [{"table": "interventions", "columns": null}], "writes": [{"table": "exhibition_record", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"renewabletypes\").where(\"dt = current_date()\").writeTo(\"meals\").append()\n", "labels": {"reads": [{"table": "renewabletypes", "columns": null}], "writes": [{"table": "meals", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO plays_games (has_access, rating) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "plays_games", "columns": ["has_access", "rating"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sale_value, pollution_id FROM animal_population LIMIT 84\")\nrows = cur.fetchall()\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "animal_population", "columns": ["sale_value", "pollution_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bridgeconstruction SELECT channel, founder FROM mentalhealthscores WHERE channel > 230\"\n", "labels": {"reads": [{"table": "mentalhealthscores", "columns": ["channel", "founder"]}], "writes": [{"table": "bridgeconstruction", "columns": ["channel", "founder"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM facility\", conn)\ndf.to_sql(\"dws.dws_events_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "facility", "columns": null}], "writes": [{"table": "dws.dws_events_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stores\").toPandas()\ndf[[\"attendee_age\", \"graphics_mode\"]].to_sql(\"strainlabresults\", engine, index=False)\n", "labels": {"reads": [{"table": "stores", "columns": null}], "writes": [{"table": "strainlabresults", "columns": ["attendee_age", "graphics_mode"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cinema SELECT goal_date, foreign, effort_name, party FROM mailshot_campaigns WHERE goal_date > 12\"\n", "labels": {"reads": [{"table": "mailshot_campaigns", "columns": ["goal_date", "foreign", "effort_name", "party"]}], "writes": [{"table": "cinema", "columns": ["goal_date", "foreign", "effort_name", "party"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"supply_chain\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"military_bases\")\n", "labels": {"reads": [{"table": "supply_chain", "columns": null}], "writes": [{"table": "military_bases", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vrgames\", conn)\ndf.to_sql(\"tournaments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vrgames", "columns": null}], "writes": [{"table": "tournaments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO astronaut_missions SELECT departmentname, region_code, organizationname, pilot_name FROM endowment WHERE departmentname > 43\")\n", "labels": {"reads": [{"table": "endowment", "columns": ["departmentname", "region_code", "organizationname", "pilot_name"]}], "writes": [{"table": "astronaut_missions", "columns": ["departmentname", "region_code", "organizationname", "pilot_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"algorithmic_fairness_incidents\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"fare_segments\")\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents", "columns": null}], "writes": [{"table": "fare_segments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"department_stores\").where(\"dt = current_date()\").writeTo(\"patient_outcomes\").append()\n", "labels": {"reads": [{"table": "department_stores", "columns": null}], "writes": [{"table": "patient_outcomes", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO defense_spending_3 (start_therapy, num_stops) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "defense_spending_3", "columns": ["start_therapy", "num_stops"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stats (file_size, visitor_country) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stats", "columns": ["file_size", "visitor_country"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO spacemissions SELECT a.special_features, b.emissions FROM dws.dws_refunds_hourly a JOIN products_booked b ON a.dno = b.dno\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_hourly", "columns": null}, {"table": "products_booked", "columns": null}], "writes": [{"table": "spacemissions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO organization SELECT 1\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_assistance\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"packages\")\n", "labels": {"reads": [{"table": "food_assistance", "columns": null}], "writes": [{"table": "packages", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table ods_sessions --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods_sessions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO injury_accident SELECT a.sustainability_id, b.time_of_purchase FROM trafficviolations a JOIN streams b ON a.reporter_id = b.reporter_id\"\n", "labels": {"reads": [{"table": "trafficviolations", "columns": null}, {"table": "streams", "columns": null}], "writes": [{"table": "injury_accident", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO collective_bargaining SELECT total_shipped, dept_store_chain_name FROM collectivebargaining WHERE total_shipped > 473\"], check=True)\n", "labels": {"reads": [{"table": "collectivebargaining", "columns": ["total_shipped", "dept_store_chain_name"]}], "writes": [{"table": "collective_bargaining", "columns": ["total_shipped", "dept_store_chain_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT amount_funded, export_country FROM africa_schema.african_mines LIMIT 25\")\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO gymnast SELECT process_id, grantid, pet_age, architect_id FROM dws.payments_delta WHERE process_id > 339\")\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["amount_funded", "export_country"]}, {"table": "dws.payments_delta", "columns": ["process_id", "grantid", "pet_age", "architect_id"]}], "writes": [{"table": "gymnast", "columns": ["process_id", "grantid", "pet_age", "architect_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model tb_reports depends on evsales\ndbt run --select tb_reports --vars '{\"src\":\"evsales\"}'\n", "labels": {"reads": [{"table": "evsales", "columns": null}], "writes": [{"table": "tb_reports", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dw.dw_events_di SELECT * FROM legacy\nspark.sql(\"INSERT INTO inventory SELECT cost, diet, defense_contractor_id FROM drills WHERE cost > 191\")\n", "labels": {"reads": [{"table": "drills", "columns": ["cost", "diet", "defense_contractor_id"]}], "writes": [{"table": "inventory", "columns": ["cost", "diet", "defense_contractor_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT categoryname, daily_sales FROM payments LIMIT 221\")\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO city SELECT export_country, daily_visitors FROM locations_oceania WHERE export_country > 477\")\n", "labels": {"reads": [{"table": "payments", "columns": ["categoryname", "daily_sales"]}, {"table": "locations_oceania", "columns": ["export_country", "daily_visitors"]}], "writes": [{"table": "city", "columns": ["export_country", "daily_visitors"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM indigenous_food_systems\", conn)\ndf.to_sql(\"price_data\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "indigenous_food_systems", "columns": null}], "writes": [{"table": "price_data", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"technology_access\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"route\")\n", "labels": {"reads": [{"table": "technology_access", "columns": null}], "writes": [{"table": "route", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"total_consumption\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ingredients\");\n", "labels": {"reads": [{"table": "total_consumption", "columns": null}], "writes": [{"table": "ingredients", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"artist_info\").toPandas()\ndf[[\"review_score\", \"vendor_name\"]].to_sql(\"materials\", engine, index=False)\n", "labels": {"reads": [{"table": "artist_info", "columns": null}], "writes": [{"table": "materials", "columns": ["review_score", "vendor_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"attendee_demographics\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table mart.mart_campaigns_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ods.products_hourly SELECT debate_id, customername FROM defense_diplomacy WHERE debate_id > 254\"\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": ["debate_id", "customername"]}], "writes": [{"table": "ods.products_hourly", "columns": ["debate_id", "customername"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model exhibitiondetails depends on intelligence_personnel\ndbt run --select exhibitiondetails --vars '{\"source_table\":\"intelligence_personnel\"}'\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": null}], "writes": [{"table": "exhibitiondetails", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads.ads_payments_hourly (job, last_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_payments_hourly", "columns": ["job", "last_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO route (machine_series, recipient_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "route", "columns": ["machine_series", "recipient_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO address (num_songs, startup_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "address", "columns": ["num_songs", "startup_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO police_emergencies SELECT field, grantid, customername, violation_type FROM ods.coupon_use WHERE field > 6\"], check=True)\n", "labels": {"reads": [{"table": "ods.coupon_use", "columns": ["field", "grantid", "customername", "violation_type"]}], "writes": [{"table": "police_emergencies", "columns": ["field", "grantid", "customername", "violation_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO customers_cards SELECT a.events, b.paritystatus FROM tokyo_motor_show a JOIN climate_investments b ON a.vendor_id = b.vendor_id\"\n", "labels": {"reads": [{"table": "tokyo_motor_show", "columns": null}, {"table": "climate_investments", "columns": null}], "writes": [{"table": "customers_cards", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO rigs SELECT * FROM legacy\ncur.execute(\"SELECT home_team_points, time_minute FROM engineer_visits LIMIT 392\")\n", "labels": {"reads": [{"table": "engineer_visits", "columns": ["home_team_points", "time_minute"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"algorithmic_fairness_incidents_monthly\")\nsave_to_output(df, \"co2_emission\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents_monthly", "columns": null}], "writes": [{"table": "co2_emission", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"film_actor\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"audience_demographics\")\n", "labels": {"reads": [{"table": "film_actor", "columns": null}], "writes": [{"table": "audience_demographics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO spacemissions SELECT customer_name, volunteerhourid, event_attendance, minename FROM ods.member_point_df WHERE customer_name > 30\"], check=True)\n", "labels": {"reads": [{"table": "ods.member_point_df", "columns": ["customer_name", "volunteerhourid", "event_attendance", "minename"]}], "writes": [{"table": "spacemissions", "columns": ["customer_name", "volunteerhourid", "event_attendance", "minename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO mining_operation SELECT * FROM legacy\nspark.sql(\"INSERT INTO bay_area_properties SELECT votes, faculty_id, line_name, ngo_id FROM ads.ads_refunds_hourly WHERE votes > 43\")\n", "labels": {"reads": [{"table": "ads.ads_refunds_hourly", "columns": ["votes", "faculty_id", "line_name", "ngo_id"]}], "writes": [{"table": "bay_area_properties", "columns": ["votes", "faculty_id", "line_name", "ngo_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO skincareinventory SELECT sales_transaction_id, outcome, asset_make, document_date FROM port WHERE sales_transaction_id > 101\");\n", "labels": {"reads": [{"table": "port", "columns": ["sales_transaction_id", "outcome", "asset_make", "document_date"]}], "writes": [{"table": "skincareinventory", "columns": ["sales_transaction_id", "outcome", "asset_make", "document_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO schools SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"contract_states\");\ndf.write().mode(\"overwrite\").saveAsTable(\"mineral_extraction\");\n", "labels": {"reads": [{"table": "contract_states", "columns": null}], "writes": [{"table": "mineral_extraction", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO episodes SELECT detention_summary, enr, playerid, state_code FROM latam_schema.education_budget WHERE detention_summary > 358\"\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": ["detention_summary", "enr", "playerid", "state_code"]}], "writes": [{"table": "episodes", "columns": ["detention_summary", "enr", "playerid", "state_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"coach\")\npush_to_store(df, \"students_enrollment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "coach", "columns": null}], "writes": [{"table": "students_enrollment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bus_routes\").where(\"dt = current_date()\").writeTo(\"influencers\").append()\n", "labels": {"reads": [{"table": "bus_routes", "columns": null}], "writes": [{"table": "influencers", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO african_union_countries SELECT supply_volume, college_id, mental_health_resource_access FROM whale_sharks WHERE supply_volume > 281\"\n", "labels": {"reads": [{"table": "whale_sharks", "columns": ["supply_volume", "college_id", "mental_health_resource_access"]}], "writes": [{"table": "african_union_countries", "columns": ["supply_volume", "college_id", "mental_health_resource_access"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"organic_products\")\nsrc.write.insertInto(\"labor_productivity\", overwrite=True)\n", "labels": {"reads": [{"table": "organic_products", "columns": null}], "writes": [{"table": "labor_productivity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO language SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ads.ads_payments_delta SELECT building_type, movie, destroyed_by_employee_id FROM europium_exports WHERE building_type > 414\")\n", "labels": {"reads": [{"table": "europium_exports", "columns": ["building_type", "movie", "destroyed_by_employee_id"]}], "writes": [{"table": "ads.ads_payments_delta", "columns": ["building_type", "movie", "destroyed_by_employee_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fashion_trend_data\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"medicine\")\n", "labels": {"reads": [{"table": "fashion_trend_data", "columns": null}], "writes": [{"table": "medicine", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"garments\").where(\"dt = current_date()\").writeTo(\"album\").append()\n", "labels": {"reads": [{"table": "garments", "columns": null}], "writes": [{"table": "album", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO social_issues SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO resource_extraction SELECT a.vegetable, b.waste_generation FROM volunteers a JOIN member_of_club b ON a.arrival_date = b.arrival_date\"\n", "labels": {"reads": [{"table": "volunteers", "columns": null}, {"table": "member_of_club", "columns": null}], "writes": [{"table": "resource_extraction", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dwd.dwd_inventory_hourly\")\nsrc.write.insertInto(\"bi.bi_inventory_delta\", overwrite=True)\n", "labels": {"reads": [{"table": "dwd.dwd_inventory_hourly", "columns": null}], "writes": [{"table": "bi.bi_inventory_delta", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO productivity SELECT * FROM legacy\nspark.sql(\"INSERT INTO crimes SELECT born_state, incidentid, end_station_id FROM supplier_ethics WHERE born_state > 95\")\n", "labels": {"reads": [{"table": "supplier_ethics", "columns": ["born_state", "incidentid", "end_station_id"]}], "writes": [{"table": "crimes", "columns": ["born_state", "incidentid", "end_station_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO claims_documents SELECT a.market, b.sales_count FROM platformi a JOIN government.city b ON a.valuation = b.valuation\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "platformi", "columns": null}, {"table": "government.city", "columns": null}], "writes": [{"table": "claims_documents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM wildlife_sanctuaries\"\n", "labels": {"reads": [{"table": "wildlife_sanctuaries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ads.risk_score\").where(\"dt = current_date()\").writeTo(\"marine_species_status\").append()\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": null}], "writes": [{"table": "marine_species_status", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_materials\").toPandas()\ndf[[\"maintenanceid\", \"custid\"]].to_sql(\"attribute_definitions\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": null}], "writes": [{"table": "attribute_definitions", "columns": ["maintenanceid", "custid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO parts SELECT * FROM legacy\nspark.sql(\"INSERT INTO aus_wellbeing SELECT electoral_register_id, client, countryname, issued_date FROM talent_acquisition WHERE electoral_register_id > 220\")\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": ["electoral_register_id", "client", "countryname", "issued_date"]}], "writes": [{"table": "aus_wellbeing", "columns": ["electoral_register_id", "client", "countryname", "issued_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 383;\nSQL\n", "labels": {"reads": [{"table": "marinespeciesobservations", "columns": ["water_consumption", "domestic_passengers"]}, {"table": "ai_ethics", "columns": ["num_sessions", "energy_efficiency_savings", "meter_300"]}], "writes": [{"table": "energy_efficiency_projects", "columns": ["num_sessions", "energy_efficiency_savings", "meter_300"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"arctic_sightings\")\npush_to_sink(df, \"commercialbuildings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "arctic_sightings", "columns": null}], "writes": [{"table": "commercialbuildings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO seal_population SELECT investment_name, base_name, funding_round_id FROM shared_ebikes WHERE investment_name > 276\"\n", "labels": {"reads": [{"table": "shared_ebikes", "columns": ["investment_name", "base_name", "funding_round_id"]}], "writes": [{"table": "seal_population", "columns": ["investment_name", "base_name", "funding_round_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT order_shipping_charges, songname FROM eco_hotels\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"dwd.dwd_users_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "eco_hotels", "columns": ["order_shipping_charges", "songname"]}], "writes": [{"table": "dwd.dwd_users_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table ship_agent --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ship_agent", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dishes SELECT cause_id, certification FROM dw.dw_member_point_hourly WHERE cause_id > 446\"\n", "labels": {"reads": [{"table": "dw.dw_member_point_hourly", "columns": ["cause_id", "certification"]}], "writes": [{"table": "dishes", "columns": ["cause_id", "certification"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO healthcare_budget SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO criminalcases SELECT num_beds, water_depth, socially_responsible, architect_id FROM county_public_safety WHERE num_beds > 255\"\n", "labels": {"reads": [{"table": "county_public_safety", "columns": ["num_beds", "water_depth", "socially_responsible", "architect_id"]}], "writes": [{"table": "criminalcases", "columns": ["num_beds", "water_depth", "socially_responsible", "architect_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 270;\nSQL\n", "labels": {"reads": [{"table": "open_pedagogy_enrollment", "columns": ["booking_date", "browser_id"]}, {"table": "temperaturehistory", "columns": ["customer_name", "area_type", "vendor_state", "production_mwh"]}], "writes": [{"table": "bi.bi_events_full", "columns": ["customer_name", "area_type", "vendor_state", "production_mwh"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mexico_regions\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "mexico_regions", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO upgrades SELECT citizens, budget_amount FROM mart.mart_shipments_hourly WHERE citizens > 411\")\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": ["citizens", "budget_amount"]}], "writes": [{"table": "upgrades", "columns": ["citizens", "budget_amount"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO marine_life_research (decor, experience_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "marine_life_research", "columns": ["decor", "experience_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO phishing_attempts SELECT * FROM legacy\nspark.sql(\"INSERT INTO artcontributors SELECT operationdate, cust_name, cinema_id, size FROM bi.member_point_full WHERE operationdate > 415\")\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": ["operationdate", "cust_name", "cinema_id", "size"]}], "writes": [{"table": "artcontributors", "columns": ["operationdate", "cust_name", "cinema_id", "size"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO disaster_response SELECT invoicedate, annual_entry_exit FROM vocals WHERE invoicedate > 406\"\n", "labels": {"reads": [{"table": "vocals", "columns": ["invoicedate", "annual_entry_exit"]}], "writes": [{"table": "disaster_response", "columns": ["invoicedate", "annual_entry_exit"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO auctions SELECT goldid, num_of_factories, acidity_level FROM ratings WHERE goldid > 160\");\n", "labels": {"reads": [{"table": "ratings", "columns": ["goldid", "num_of_factories", "acidity_level"]}], "writes": [{"table": "auctions", "columns": ["goldid", "num_of_factories", "acidity_level"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 177;\nEOF\n", "labels": {"reads": [{"table": "companies_extended", "columns": ["consultations", "container_id", "tripdatetime"]}], "writes": [{"table": "platform", "columns": ["consultations", "container_id", "tripdatetime"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO environmentalimpact SELECT address, province FROM birds WHERE address > 410\")\n", "labels": {"reads": [{"table": "birds", "columns": ["address", "province"]}], "writes": [{"table": "environmentalimpact", "columns": ["address", "province"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dwd.dwd_risk_score_delta\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"rental\");\ndf.write().mode(\"overwrite\").saveAsTable(\"workout_sessions\");\n", "labels": {"reads": [{"table": "rental", "columns": null}], "writes": [{"table": "workout_sessions", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO victims SELECT playerid, unit_of_measure, drought_id, priceid FROM recycling_centers WHERE playerid > 386\");\n", "labels": {"reads": [{"table": "recycling_centers", "columns": ["playerid", "unit_of_measure", "drought_id", "priceid"]}], "writes": [{"table": "victims", "columns": ["playerid", "unit_of_measure", "drought_id", "priceid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dailystreams depends on book\ndbt build -s dailystreams --vars 'source: book'\n", "labels": {"reads": [{"table": "book", "columns": null}], "writes": [{"table": "dailystreams", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table affordablehousing --columns launch_year,opened_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "affordablehousing", "columns": ["launch_year", "opened_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.bedroom_count > 329).all()\n# src table: bi.bi_campaigns_delta\nengine.execute(\"INSERT INTO e_scooter_trips SELECT * FROM bi.bi_campaigns_delta\")\n", "labels": {"reads": [{"table": "bi.bi_campaigns_delta", "columns": null}], "writes": [{"table": "e_scooter_trips", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO trust SELECT water_temp, pipeline_name FROM asia_events WHERE water_temp > 484\"\n", "labels": {"reads": [{"table": "asia_events", "columns": ["water_temp", "pipeline_name"]}], "writes": [{"table": "trust", "columns": ["water_temp", "pipeline_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"dw_users_full\")\nupsert_to_sink(df, \"carbon_offset_programs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dw_users_full", "columns": null}], "writes": [{"table": "carbon_offset_programs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM musicsales\"\n", "labels": {"reads": [{"table": "musicsales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"fabric\")\nsrc.write.insertInto(\"education\", overwrite=True)\n", "labels": {"reads": [{"table": "fabric", "columns": null}], "writes": [{"table": "education", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM crypto_transactions\"\n", "labels": {"reads": [{"table": "crypto_transactions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ads_payments_di\", conn)\ndf.to_sql(\"equipment_maintenance\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": null}], "writes": [{"table": "equipment_maintenance", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO humanitarian_aid SELECT field_id, fault_log_entry_id, vendorid FROM athlete_wellbeing WHERE field_id > 490\"\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": ["field_id", "fault_log_entry_id", "vendorid"]}], "writes": [{"table": "humanitarian_aid", "columns": ["field_id", "fault_log_entry_id", "vendorid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM market_share\", conn)\ndf.to_sql(\"investment_rounds\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "market_share", "columns": null}], "writes": [{"table": "investment_rounds", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ocean_health_monitor (last_updated, is_compliant) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ocean_health_monitor", "columns": ["last_updated", "is_compliant"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"faculty\")\nsrc.write.insertInto(\"tasks\", overwrite=True)\n", "labels": {"reads": [{"table": "faculty", "columns": null}], "writes": [{"table": "tasks", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT product_category, court_id FROM trafficviolations\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"greenbuildings\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "trafficviolations", "columns": ["product_category", "court_id"]}], "writes": [{"table": "greenbuildings", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM architect\"\n", "labels": {"reads": [{"table": "architect", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 410;\nSQL\n", "labels": {"reads": [{"table": "haircare_sales", "columns": ["council_tax_id", "potency"]}, {"table": "ods.ods_risk_score_full", "columns": ["ingredient", "pricepergram", "vin"]}], "writes": [{"table": "player_f", "columns": ["ingredient", "pricepergram", "vin"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO train_station SELECT num_pallets, fare_date FROM all_star WHERE num_pallets > 343\")\n", "labels": {"reads": [{"table": "all_star", "columns": ["num_pallets", "fare_date"]}], "writes": [{"table": "train_station", "columns": ["num_pallets", "fare_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_risk_score_delta --columns rebounds,shipment_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_risk_score_delta", "columns": ["rebounds", "shipment_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"prison\")\nsrc.write.insertInto(\"ai_safety_incidents\", overwrite=True)\n", "labels": {"reads": [{"table": "prison", "columns": null}], "writes": [{"table": "ai_safety_incidents", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"restorative_justice_sentences\").where(\"dt = current_date()\").writeTo(\"ethicalaibudget\").append()\n", "labels": {"reads": [{"table": "restorative_justice_sentences", "columns": null}], "writes": [{"table": "ethicalaibudget", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"artwork_styles\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "artwork_styles", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ocean_acidification SELECT * FROM legacy\nspark.sql(\"INSERT INTO site SELECT description, detection_date FROM candidates WHERE description > 370\")\n", "labels": {"reads": [{"table": "candidates", "columns": ["description", "detection_date"]}], "writes": [{"table": "site", "columns": ["description", "detection_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT neighborhood, task FROM conservation LIMIT 227\")\nmetrics.append(round(score, 4))\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO exhibition_record SELECT machine, saleamount FROM programoutcomes WHERE machine > 219\")\n", "labels": {"reads": [{"table": "conservation", "columns": ["neighborhood", "task"]}, {"table": "programoutcomes", "columns": ["machine", "saleamount"]}], "writes": [{"table": "exhibition_record", "columns": ["machine", "saleamount"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO exhibition_visitors SELECT a.i_id, b.requestid FROM asia_events a JOIN social_good_projects b ON a.voter_id = b.voter_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "asia_events", "columns": null}, {"table": "social_good_projects", "columns": null}], "writes": [{"table": "exhibition_visitors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO tv_shows_genre (restock_date, asset_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "tv_shows_genre", "columns": ["restock_date", "asset_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model issues depends on shipment_data\ndbt build -s issues --vars 'source: shipment_data'\n", "labels": {"reads": [{"table": "shipment_data", "columns": null}], "writes": [{"table": "issues", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"astronauts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dws.risk_score_daily\")\n", "labels": {"reads": [{"table": "astronauts", "columns": null}], "writes": [{"table": "dws.risk_score_daily", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO deliveryaddresses SELECT serviceid, bats, booked_count FROM sustainableprojects WHERE serviceid > 270\");\n", "labels": {"reads": [{"table": "sustainableprojects", "columns": ["serviceid", "bats", "booked_count"]}], "writes": [{"table": "deliveryaddresses", "columns": ["serviceid", "bats", "booked_count"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO apartment_bookings SELECT rom_mib, denomination FROM recyclers WHERE rom_mib > 176\"], check=True)\n", "labels": {"reads": [{"table": "recyclers", "columns": ["rom_mib", "denomination"]}], "writes": [{"table": "apartment_bookings", "columns": ["rom_mib", "denomination"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"infrastructureprojects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 126;\nSQL\n", "labels": {"reads": [{"table": "campaigns_2023", "columns": ["artpieceid", "author_or_editor"]}, {"table": "bi.products_daily", "columns": ["seating", "famous_title", "num_investments"]}], "writes": [{"table": "furniture", "columns": ["seating", "famous_title", "num_investments"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO astronaut_medical_3 SELECT official_native_language, lesson_id, production_usage, project_name FROM landfills WHERE official_native_language > 29\");\n", "labels": {"reads": [{"table": "landfills", "columns": ["official_native_language", "lesson_id", "production_usage", "project_name"]}], "writes": [{"table": "astronaut_medical_3", "columns": ["official_native_language", "lesson_id", "production_usage", "project_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"socially_responsible_loans\")\n", "labels": {"reads": [{"table": "rural_projects", "columns": null}], "writes": [{"table": "socially_responsible_loans", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"authors\").where(\"dt = current_date()\").writeTo(\"bi.users_full\").append()\n", "labels": {"reads": [{"table": "authors", "columns": null}], "writes": [{"table": "bi.users_full", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ods.ods_risk_score_full SELECT do_value, away_team_three_point, end_station_name FROM artpieces WHERE do_value > 220\"\n", "labels": {"reads": [{"table": "artpieces", "columns": ["do_value", "away_team_three_point", "end_station_name"]}], "writes": [{"table": "ods.ods_risk_score_full", "columns": ["do_value", "away_team_three_point", "end_station_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO ods_clicks_df SELECT manager_name, strainname, region_name, personnel FROM time_dim WHERE manager_name > 256\");\n", "labels": {"reads": [{"table": "time_dim", "columns": ["manager_name", "strainname", "region_name", "personnel"]}], "writes": [{"table": "ods_clicks_df", "columns": ["manager_name", "strainname", "region_name", "personnel"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dw.dw_inventory_df\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dw.dw_inventory_df", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT trade, item_price FROM dwd.vendors\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"apac_hotel_views\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dwd.vendors", "columns": ["trade", "item_price"]}], "writes": [{"table": "apac_hotel_views", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"department_stores\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "department_stores", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM incarcerated\"\n", "labels": {"reads": [{"table": "incarcerated", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT mental_health_resource_access, transaction_date FROM supplier_ethics LIMIT 473\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "supplier_ethics", "columns": ["mental_health_resource_access", "transaction_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.communityid > 88).all()\n# src table: rebounds\nengine.execute(\"INSERT INTO public.police_calls SELECT * FROM rebounds\")\n", "labels": {"reads": [{"table": "rebounds", "columns": null}], "writes": [{"table": "public.police_calls", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO pollution_control_initiatives (mission_category, ingredient) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "pollution_control_initiatives", "columns": ["mission_category", "ingredient"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM art_pieces\", conn)\ndf.to_sql(\"apartment_facilities\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "art_pieces", "columns": null}], "writes": [{"table": "apartment_facilities", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 104;\nEOF\n", "labels": {"reads": [{"table": "stg.stg_users_full", "columns": ["amount_used", "process_id", "city_population", "seasons"]}], "writes": [{"table": "mental_health_professionals_2", "columns": ["amount_used", "process_id", "city_population", "seasons"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"farms\").toPandas()\ndf[[\"profits_in_billion\", \"fund_name\"]].to_sql(\"pollution_initiatives\", engine, index=False)\n", "labels": {"reads": [{"table": "farms", "columns": null}], "writes": [{"table": "pollution_initiatives", "columns": ["profits_in_billion", "fund_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO forest_species (goldquantity, genrename) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "forest_species", "columns": ["goldquantity", "genrename"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO patient_satisfaction (park_id, guest_last_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "patient_satisfaction", "columns": ["park_id", "guest_last_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mining_operations\", conn)\ndf.to_sql(\"aircraft_flights\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mining_operations", "columns": null}], "writes": [{"table": "aircraft_flights", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO chemical_processes SELECT inspection_id, away_team_score FROM culturalcompetencytrainings WHERE inspection_id > 2\"], check=True)\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": ["inspection_id", "away_team_score"]}], "writes": [{"table": "chemical_processes", "columns": ["inspection_id", "away_team_score"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO species_data (program_name, funding) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "species_data", "columns": ["program_name", "funding"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"web_client_accelerator\").where(\"dt = current_date()\").writeTo(\"flu_cases\").append()\n", "labels": {"reads": [{"table": "web_client_accelerator", "columns": null}], "writes": [{"table": "flu_cases", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM appointment\", conn)\ndf.to_sql(\"bi.bi_events_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "appointment", "columns": null}], "writes": [{"table": "bi.bi_events_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"behavior_incident\").where(\"dt = current_date()\").writeTo(\"athletes\").append()\n", "labels": {"reads": [{"table": "behavior_incident", "columns": null}], "writes": [{"table": "athletes", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO ucl_top10 SELECT water_consumption, emissions FROM film_category WHERE water_consumption > 115\")\n", "labels": {"reads": [{"table": "film_category", "columns": ["water_consumption", "emissions"]}], "writes": [{"table": "ucl_top10", "columns": ["water_consumption", "emissions"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO trade_history SELECT trial_success_rate, event_date, graduate, province_id FROM stg.orders_daily WHERE trial_success_rate > 254\");\n", "labels": {"reads": [{"table": "stg.orders_daily", "columns": ["trial_success_rate", "event_date", "graduate", "province_id"]}], "writes": [{"table": "trade_history", "columns": ["trial_success_rate", "event_date", "graduate", "province_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT exit_type, damage_millions_usd FROM autoshow LIMIT 114\")\nimport logging\nspark.sql(\"INSERT INTO food_safety_inspections SELECT last_maintenance_date, min_salary FROM dws.dws_orders_full WHERE last_maintenance_date > 359\")\n", "labels": {"reads": [{"table": "autoshow", "columns": ["exit_type", "damage_millions_usd"]}, {"table": "dws.dws_orders_full", "columns": ["last_maintenance_date", "min_salary"]}], "writes": [{"table": "food_safety_inspections", "columns": ["last_maintenance_date", "min_salary"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg.stg_users (createdate, volunteerjoindate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_users", "columns": ["createdate", "volunteerjoindate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.firstdonationdate > 354).all()\n# src table: video_content\nengine.execute(\"INSERT INTO fairtradecertification SELECT * FROM video_content\")\n", "labels": {"reads": [{"table": "video_content", "columns": null}], "writes": [{"table": "fairtradecertification", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.train_number > 303).all()\n# src table: member_details\nengine.execute(\"INSERT INTO fund_investments SELECT * FROM member_details\")\n", "labels": {"reads": [{"table": "member_details", "columns": null}], "writes": [{"table": "fund_investments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"attorney_billing\")\nsrc.write.insertInto(\"site\", overwrite=True)\n", "labels": {"reads": [{"table": "attorney_billing", "columns": null}], "writes": [{"table": "site", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO paper_data SELECT stateid, operation, mode FROM publicchargingstations WHERE stateid > 213\"\n", "labels": {"reads": [{"table": "publicchargingstations", "columns": ["stateid", "operation", "mode"]}], "writes": [{"table": "paper_data", "columns": ["stateid", "operation", "mode"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM singer_in_concert\", conn)\ndf.to_sql(\"user_workouts_march\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": [{"table": "user_workouts_march", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads.ads_risk_score_hourly\"\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"research_staff\").where(\"dt = current_date()\").writeTo(\"ethicalaibudget\").append()\n", "labels": {"reads": [{"table": "research_staff", "columns": null}], "writes": [{"table": "ethicalaibudget", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"artifacts\").where(\"dt = current_date()\").writeTo(\"irrigation_systems\").append()\n", "labels": {"reads": [{"table": "artifacts", "columns": null}], "writes": [{"table": "irrigation_systems", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"patienttreatments\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"dw.dw_events_di\")\n", "labels": {"reads": [{"table": "patienttreatments", "columns": null}], "writes": [{"table": "dw.dw_events_di", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO member_attendance SELECT outcome_code, device_name, restaurantname FROM retail_workers_union WHERE outcome_code > 470\"\n", "labels": {"reads": [{"table": "retail_workers_union", "columns": ["outcome_code", "device_name", "restaurantname"]}], "writes": [{"table": "member_attendance", "columns": ["outcome_code", "device_name", "restaurantname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"tech_accessibility_funding\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO vessel_registry SELECT schedule_id, injury FROM stg.sessions_full WHERE schedule_id > 53\"], check=True)\n", "labels": {"reads": [{"table": "stg.sessions_full", "columns": ["schedule_id", "injury"]}], "writes": [{"table": "vessel_registry", "columns": ["schedule_id", "injury"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO community_centers SELECT a.chemical_name, b.union_member_id FROM mine_workforce a JOIN dwd.dwd_exposure_full b ON a.building_address = b.building_address\"\n", "labels": {"reads": [{"table": "mine_workforce", "columns": null}, {"table": "dwd.dwd_exposure_full", "columns": null}], "writes": [{"table": "community_centers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO animal_population_status SELECT stat_id, passenger_id, court_appearances FROM user_workouts_march WHERE stat_id > 406\")\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": ["stat_id", "passenger_id", "court_appearances"]}], "writes": [{"table": "animal_population_status", "columns": ["stat_id", "passenger_id", "court_appearances"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO budgets SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT detention_summary, fundingagency FROM policyholders LIMIT 131\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "policyholders", "columns": ["detention_summary", "fundingagency"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"pollutionincidents\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "pollutionincidents", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO mart.shipments_delta SELECT unit_id, annual_interchanges FROM appellations WHERE unit_id > 61\")\n", "labels": {"reads": [{"table": "appellations", "columns": ["unit_id", "annual_interchanges"]}], "writes": [{"table": "mart.shipments_delta", "columns": ["unit_id", "annual_interchanges"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"underwater_trenches\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ocean_floor_mapping\")\n", "labels": {"reads": [{"table": "underwater_trenches", "columns": null}], "writes": [{"table": "ocean_floor_mapping", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT medication, export_country FROM casesbyyear LIMIT 449\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "casesbyyear", "columns": ["medication", "export_country"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"construction_labor\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mining_operation\")\n", "labels": {"reads": [{"table": "construction_labor", "columns": null}], "writes": [{"table": "mining_operation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT exploited, college_location FROM mars_rovers\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"train_station\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mars_rovers", "columns": ["exploited", "college_location"]}], "writes": [{"table": "train_station", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_products_hourly\").toPandas()\ndf[[\"fairtrade\", \"stu_hrs\"]].to_sql(\"militarydrones\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": null}], "writes": [{"table": "militarydrones", "columns": ["fairtrade", "stu_hrs"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM nursing_homes\", conn)\ndf.to_sql(\"carbon_sequestration\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "nursing_homes", "columns": null}], "writes": [{"table": "carbon_sequestration", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table smart_contracts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "smart_contracts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO member SELECT garment_name, recipe_id FROM repair_assignment WHERE garment_name > 138\")\n", "labels": {"reads": [{"table": "repair_assignment", "columns": ["garment_name", "recipe_id"]}], "writes": [{"table": "member", "columns": ["garment_name", "recipe_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"carbon_offset_programs\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.catalog_id > 358).all()\n# src table: dorm_amenity\nengine.execute(\"INSERT INTO athlete_stats SELECT * FROM dorm_amenity\")\n", "labels": {"reads": [{"table": "dorm_amenity", "columns": null}], "writes": [{"table": "athlete_stats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO circulation_history SELECT * FROM legacy\ncur.execute(\"SELECT artist_name, marketing_region_code FROM member_data LIMIT 499\")\n", "labels": {"reads": [{"table": "member_data", "columns": ["artist_name", "marketing_region_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"labor_unions\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ca_menu_items\");\n", "labels": {"reads": [{"table": "labor_unions", "columns": null}], "writes": [{"table": "ca_menu_items", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 476;\nSQL\n", "labels": {"reads": [{"table": "haircare_sales", "columns": ["organization_id", "bioprocess_name"]}, {"table": "hotel_chains", "columns": ["host_id", "fundingagency"]}], "writes": [{"table": "market_access", "columns": ["host_id", "fundingagency"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart_events_full SELECT a.countryid, b.stu_hrs FROM militarybases a JOIN oil_production b ON a.testdate = b.testdate\"\n", "labels": {"reads": [{"table": "militarybases", "columns": null}, {"table": "oil_production", "columns": null}], "writes": [{"table": "mart_events_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.destination_name > 265).all()\n# src table: audience\nengine.execute(\"INSERT INTO certifications SELECT * FROM audience\")\n", "labels": {"reads": [{"table": "audience", "columns": null}], "writes": [{"table": "certifications", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO bi.bi_sessions_hourly SELECT item_sold, pd_id, financial_wellbeing_score, conferencename FROM wedding WHERE item_sold > 254\")\n", "labels": {"reads": [{"table": "wedding", "columns": ["item_sold", "pd_id", "financial_wellbeing_score", "conferencename"]}], "writes": [{"table": "bi.bi_sessions_hourly", "columns": ["item_sold", "pd_id", "financial_wellbeing_score", "conferencename"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"cloud_issues\")\nsrc.write.insertInto(\"user_ad_interactions\", overwrite=True)\n", "labels": {"reads": [{"table": "cloud_issues", "columns": null}], "writes": [{"table": "user_ad_interactions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO armed_forces (date_in_locaton_to, testtypeid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "armed_forces", "columns": ["date_in_locaton_to", "testtypeid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"flights\");\ndf.write().mode(\"overwrite\").saveAsTable(\"customers_policies\");\n", "labels": {"reads": [{"table": "flights", "columns": null}], "writes": [{"table": "customers_policies", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"government.city\").toPandas()\ndf[[\"dock_count\", \"crs_description\"]].to_sql(\"drugs\", engine, index=False)\n", "labels": {"reads": [{"table": "government.city", "columns": null}], "writes": [{"table": "drugs", "columns": ["dock_count", "crs_description"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"algorithmic_fairness_incidents_monthly\").toPandas()\ndf[[\"disease\", \"carbon_offset_tons\"]].to_sql(\"innovation_trends\", engine, index=False)\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents_monthly", "columns": null}], "writes": [{"table": "innovation_trends", "columns": ["disease", "carbon_offset_tons"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"distributors\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"economic_diversification\")\n", "labels": {"reads": [{"table": "distributors", "columns": null}], "writes": [{"table": "economic_diversification", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vehiclemodels SELECT a.attribute_data_type, b.workeridentity FROM ods.ods_exposure_delta a JOIN affiliated_with b ON a.age_group_id = b.age_group_id\"\n", "labels": {"reads": [{"table": "ods.ods_exposure_delta", "columns": null}, {"table": "affiliated_with", "columns": null}], "writes": [{"table": "vehiclemodels", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"sustainable_fabrics\");\ndf.write().mode(\"overwrite\").saveAsTable(\"haircare_sales\");\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": null}], "writes": [{"table": "haircare_sales", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO building SELECT investment_type, chemical_id, categoryname FROM carbon_pricing WHERE investment_type > 459\");\n", "labels": {"reads": [{"table": "carbon_pricing", "columns": ["investment_type", "chemical_id", "categoryname"]}], "writes": [{"table": "building", "columns": ["investment_type", "chemical_id", "categoryname"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 306;\nSQL\n", "labels": {"reads": [{"table": "recycling_centers", "columns": ["college_id", "applicant"]}, {"table": "carbon_offset_programs", "columns": ["machine", "num_attendees", "next_maintenance", "process_id"]}], "writes": [{"table": "flights", "columns": ["machine", "num_attendees", "next_maintenance", "process_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT mine_name, energy_generated FROM phishing_targets\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"eco_hotels\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "phishing_targets", "columns": ["mine_name", "energy_generated"]}], "writes": [{"table": "eco_hotels", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table pipelines --target-dir /tmp/land\n", "labels": {"reads": [{"table": "pipelines", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model program_budget depends on match\ndbt build -s program_budget --vars '{\"src\":\"match\"}'\n", "labels": {"reads": [{"table": "match", "columns": null}], "writes": [{"table": "program_budget", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sustainable_warehouses\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"architect\")\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": null}], "writes": [{"table": "architect", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO packages SELECT attendance, stream_date, program_expenses FROM genetics.crispr WHERE attendance > 416\"], check=True)\n", "labels": {"reads": [{"table": "genetics.crispr", "columns": ["attendance", "stream_date", "program_expenses"]}], "writes": [{"table": "packages", "columns": ["attendance", "stream_date", "program_expenses"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"intelligence_agents\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.bi_events_df\")\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": null}], "writes": [{"table": "bi.bi_events_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT satellite, mouse_id FROM waterconservationinitiatives LIMIT 32\")\nimport logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO energy_efficiency_projects SELECT award, intervention_type, ai_id, min_depth FROM swimmer WHERE award > 218\")\n", "labels": {"reads": [{"table": "waterconservationinitiatives", "columns": ["satellite", "mouse_id"]}, {"table": "swimmer", "columns": ["award", "intervention_type", "ai_id", "min_depth"]}], "writes": [{"table": "energy_efficiency_projects", "columns": ["award", "intervention_type", "ai_id", "min_depth"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model emissions depends on healthcare_system\ndbt run -s emissions --vars '{\"src\":\"healthcare_system\"}'\n", "labels": {"reads": [{"table": "healthcare_system", "columns": null}], "writes": [{"table": "emissions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT status_code, safetytestdate FROM menu_items LIMIT 490\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO electric_vehicles SELECT transaction_value, rural_area FROM courtcases WHERE transaction_value > 157\")\n", "labels": {"reads": [{"table": "menu_items", "columns": ["status_code", "safetytestdate"]}, {"table": "courtcases", "columns": ["transaction_value", "rural_area"]}], "writes": [{"table": "electric_vehicles", "columns": ["transaction_value", "rural_area"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO lenders SELECT a.studentid, b.social_impact_score FROM singer_in_concert a JOIN cybersecurity_incidents b ON a.event_id = b.event_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}, {"table": "cybersecurity_incidents", "columns": null}], "writes": [{"table": "lenders", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model video_content depends on provider_training\ndbt run -s video_content --vars 'source: provider_training'\n", "labels": {"reads": [{"table": "provider_training", "columns": null}], "writes": [{"table": "video_content", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO home_game SELECT domestic_passengers, rooms, union_members FROM agriculturalinvestments WHERE domestic_passengers > 432\"\n", "labels": {"reads": [{"table": "agriculturalinvestments", "columns": ["domestic_passengers", "rooms", "union_members"]}], "writes": [{"table": "home_game", "columns": ["domestic_passengers", "rooms", "union_members"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO impact_investments SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO job_postings SELECT treatment_name, baseprice FROM defenseprojects WHERE treatment_name > 447\"\n", "labels": {"reads": [{"table": "defenseprojects", "columns": ["treatment_name", "baseprice"]}], "writes": [{"table": "job_postings", "columns": ["treatment_name", "baseprice"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model bi_sessions_daily depends on stg.stg_risk_score_hourly\ndbt run --select bi_sessions_daily --vars 'source: stg.stg_risk_score_hourly'\n", "labels": {"reads": [{"table": "stg.stg_risk_score_hourly", "columns": null}], "writes": [{"table": "bi_sessions_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.building_name > 248).all()\n# src table: mediators\nengine.execute(\"INSERT INTO experience SELECT * FROM mediators\")\n", "labels": {"reads": [{"table": "mediators", "columns": null}], "writes": [{"table": "experience", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO sustainable_menu_items SELECT songid, unionid FROM airlines WHERE songid > 201\");\n", "labels": {"reads": [{"table": "airlines", "columns": ["songid", "unionid"]}], "writes": [{"table": "sustainable_menu_items", "columns": ["songid", "unionid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_orders_delta SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"accelerator_compatible_browser\")\nsrc.write.insertInto(\"class\", overwrite=True)\n", "labels": {"reads": [{"table": "accelerator_compatible_browser", "columns": null}], "writes": [{"table": "class", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"fabric\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "fabric", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO crop_temperature (year_opened, stars) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "crop_temperature", "columns": ["year_opened", "stars"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO tokyo_motor_show SELECT a.fan_name, b.institution_name FROM bike_share a JOIN textile_sourcing b ON a.competition = b.competition\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bike_share", "columns": null}, {"table": "textile_sourcing", "columns": null}], "writes": [{"table": "tokyo_motor_show", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.store_email_address > 162).all()\n# src table: ais\nengine.execute(\"INSERT INTO fair_wages SELECT * FROM ais\")\n", "labels": {"reads": [{"table": "ais", "columns": null}], "writes": [{"table": "fair_wages", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model seamounts depends on malicious_activity\ndbt run -s seamounts --vars 'source: malicious_activity'\n", "labels": {"reads": [{"table": "malicious_activity", "columns": null}], "writes": [{"table": "seamounts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT founder_count, enddate FROM player_sessions LIMIT 464\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO customer_master_index SELECT visit_date, store_email_address, mhw_id FROM rating WHERE visit_date > 181\")\n", "labels": {"reads": [{"table": "player_sessions", "columns": ["founder_count", "enddate"]}, {"table": "rating", "columns": ["visit_date", "store_email_address", "mhw_id"]}], "writes": [{"table": "customer_master_index", "columns": ["visit_date", "store_email_address", "mhw_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table date --target-dir /tmp/land\n", "labels": {"reads": [{"table": "date", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT label_id, shares FROM pets LIMIT 362\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO plots SELECT hometeam, trainingid FROM product_ingredient WHERE hometeam > 157\")\n", "labels": {"reads": [{"table": "pets", "columns": ["label_id", "shares"]}, {"table": "product_ingredient", "columns": ["hometeam", "trainingid"]}], "writes": [{"table": "plots", "columns": ["hometeam", "trainingid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"buildingpermits\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"directors\")\n", "labels": {"reads": [{"table": "buildingpermits", "columns": null}], "writes": [{"table": "directors", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT clinic_name, veteran_id FROM operate_company LIMIT 473\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "operate_company", "columns": ["clinic_name", "veteran_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ethicalaibudget\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.events_delta\").toPandas()\ndf[[\"partitionid\", \"co2_reduction\"]].to_sql(\"race\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": null}], "writes": [{"table": "race", "columns": ["partitionid", "co2_reduction"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM round\"\n", "labels": {"reads": [{"table": "round", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"tourist_attraction_features\")\nsrc.write.insertInto(\"southeast_providers\", overwrite=True)\n", "labels": {"reads": [{"table": "tourist_attraction_features", "columns": null}], "writes": [{"table": "southeast_providers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT review_date, media_type_id FROM manufacturermaterials LIMIT 193\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "manufacturermaterials", "columns": ["review_date", "media_type_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model sites depends on market_trends\ndbt build -s sites --vars '{\"src\":\"market_trends\"}'\n", "labels": {"reads": [{"table": "market_trends", "columns": null}], "writes": [{"table": "sites", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO soccer_teams (impressions, employee_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "soccer_teams", "columns": ["impressions", "employee_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO states SELECT a.headquartered_city, b.access_count FROM seafood a JOIN bias_categories b ON a.rooms = b.rooms\"\n", "labels": {"reads": [{"table": "seafood", "columns": null}, {"table": "bias_categories", "columns": null}], "writes": [{"table": "states", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO materials_usage (event_id, max_salary) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "materials_usage", "columns": ["event_id", "max_salary"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO eventattendance SELECT * FROM legacy\nspark.sql(\"INSERT INTO opendatainitiatives SELECT regional_population, maintenance_type FROM redundant_billing_data WHERE regional_population > 175\")\n", "labels": {"reads": [{"table": "redundant_billing_data", "columns": ["regional_population", "maintenance_type"]}], "writes": [{"table": "opendatainitiatives", "columns": ["regional_population", "maintenance_type"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO rebounds SELECT problem_log_id, cargo, detention_type_code, incident_name FROM world_heritage_sites WHERE problem_log_id > 475\")\n", "labels": {"reads": [{"table": "world_heritage_sites", "columns": ["problem_log_id", "cargo", "detention_type_code", "incident_name"]}], "writes": [{"table": "rebounds", "columns": ["problem_log_id", "cargo", "detention_type_code", "incident_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO traditional_arts SELECT reported_date, coal_reserve_remaining FROM bi.risk_score_df WHERE reported_date > 16\"\n", "labels": {"reads": [{"table": "bi.risk_score_df", "columns": ["reported_date", "coal_reserve_remaining"]}], "writes": [{"table": "traditional_arts", "columns": ["reported_date", "coal_reserve_remaining"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"european_healthcare\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "european_healthcare", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"surveylocations\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"autonomous_research\")\n", "labels": {"reads": [{"table": "surveylocations", "columns": null}], "writes": [{"table": "autonomous_research", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT date_became_customer, fair_trade FROM union_membership LIMIT 43\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO market_trends SELECT purchase_date, installation_date, donationamount FROM accounts WHERE purchase_date > 50\")\n", "labels": {"reads": [{"table": "union_membership", "columns": ["date_became_customer", "fair_trade"]}, {"table": "accounts", "columns": ["purchase_date", "installation_date", "donationamount"]}], "writes": [{"table": "market_trends", "columns": ["purchase_date", "installation_date", "donationamount"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO contract_states SELECT transactions, is_deforested, community_type FROM branch WHERE transactions > 224\"], check=True)\n", "labels": {"reads": [{"table": "branch", "columns": ["transactions", "is_deforested", "community_type"]}], "writes": [{"table": "contract_states", "columns": ["transactions", "is_deforested", "community_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT co2_amount, restaurant_name FROM excavationsites LIMIT 297\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "excavationsites", "columns": ["co2_amount", "restaurant_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO trends_2022 SELECT total_attendance, fabrictype, card_number, update_date FROM ref_document_status WHERE total_attendance > 71\")\n", "labels": {"reads": [{"table": "ref_document_status", "columns": ["total_attendance", "fabrictype", "card_number", "update_date"]}], "writes": [{"table": "trends_2022", "columns": ["total_attendance", "fabrictype", "card_number", "update_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model asteroids depends on nomination\ndbt build -s asteroids --vars '{\"source_table\":\"nomination\"}'\n", "labels": {"reads": [{"table": "nomination", "columns": null}], "writes": [{"table": "asteroids", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO al_jazeera_data SELECT bicycle_id, inspection_date, membername, donationid FROM company WHERE bicycle_id > 162\")\n", "labels": {"reads": [{"table": "company", "columns": ["bicycle_id", "inspection_date", "membername", "donationid"]}], "writes": [{"table": "al_jazeera_data", "columns": ["bicycle_id", "inspection_date", "membername", "donationid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ingredients SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO fleet_management SELECT kills, fine, cargo_type, unit_of_measure FROM dwd.dwd_users_hourly WHERE kills > 267\"\n", "labels": {"reads": [{"table": "dwd.dwd_users_hourly", "columns": ["kills", "fine", "cargo_type", "unit_of_measure"]}], "writes": [{"table": "fleet_management", "columns": ["kills", "fine", "cargo_type", "unit_of_measure"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"multimodal_trips\").where(\"dt = current_date()\").writeTo(\"infra_diversification\").append()\n", "labels": {"reads": [{"table": "multimodal_trips", "columns": null}], "writes": [{"table": "infra_diversification", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO diversification_projects SELECT a.sd_id, b.worker_name FROM deliveryaddresses a JOIN spacecraft_manufacturers b ON a.mental_health_status = b.mental_health_status\"\n", "labels": {"reads": [{"table": "deliveryaddresses", "columns": null}, {"table": "spacecraft_manufacturers", "columns": null}], "writes": [{"table": "diversification_projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"artist_data\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "artist_data", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO news_views SELECT max_salary, artwork_name FROM marine_species_indian WHERE max_salary > 218\"\n", "labels": {"reads": [{"table": "marine_species_indian", "columns": ["max_salary", "artwork_name"]}], "writes": [{"table": "news_views", "columns": ["max_salary", "artwork_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO algorithmic_fairness_incidents_monthly SELECT produceid, actor_id, min_temperature_f, fault_short_name FROM laptimes WHERE produceid > 227\"\n", "labels": {"reads": [{"table": "laptimes", "columns": ["produceid", "actor_id", "min_temperature_f", "fault_short_name"]}], "writes": [{"table": "algorithmic_fairness_incidents_monthly", "columns": ["produceid", "actor_id", "min_temperature_f", "fault_short_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"communities\");\ndf.write().mode(\"overwrite\").saveAsTable(\"continent\");\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": [{"table": "continent", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.shipments_delta SELECT fish_count, ai_adoption, functional_area_description, account_details FROM counties WHERE fish_count > 164\"], check=True)\n", "labels": {"reads": [{"table": "counties", "columns": ["fish_count", "ai_adoption", "functional_area_description", "account_details"]}], "writes": [{"table": "mart.shipments_delta", "columns": ["fish_count", "ai_adoption", "functional_area_description", "account_details"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO agency_satellites (copy_number, health_equity_metric_3) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "agency_satellites", "columns": ["copy_number", "health_equity_metric_3"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO courtcases (decision, contract_start_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "courtcases", "columns": ["decision", "contract_start_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO conservation_initiatives SELECT a.sd_id, b.policyname FROM property_community a JOIN spacecraft_temperatures b ON a.sales_amount = b.sales_amount\"\n", "labels": {"reads": [{"table": "property_community", "columns": null}, {"table": "spacecraft_temperatures", "columns": null}], "writes": [{"table": "conservation_initiatives", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO underwater_cables SELECT reason, co2_amount, daily_co2_emission FROM country_labor WHERE reason > 158\"\n", "labels": {"reads": [{"table": "country_labor", "columns": ["reason", "co2_amount", "daily_co2_emission"]}], "writes": [{"table": "underwater_cables", "columns": ["reason", "co2_amount", "daily_co2_emission"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 214;\nEOF\n", "labels": {"reads": [{"table": "forestry_practices", "columns": ["aid", "asset_disposed_date", "weight", "client"]}], "writes": [{"table": "offender_demographics", "columns": ["aid", "asset_disposed_date", "weight", "client"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dept_store_chain_id, strain_id FROM date LIMIT 49\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "date", "columns": ["dept_store_chain_id", "strain_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO exit SELECT a.led_by, b.complete_date FROM catalogs a JOIN game_sessions b ON a.socialimpactscore = b.socialimpactscore\"\n", "labels": {"reads": [{"table": "catalogs", "columns": null}, {"table": "game_sessions", "columns": null}], "writes": [{"table": "exit", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table garmentproduction --target-dir /tmp/land\n", "labels": {"reads": [{"table": "garmentproduction", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"daily_revenue\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "daily_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO electric_vehicle_stats SELECT target_name, certification_id, port_id FROM food_justice_orgs WHERE target_name > 303\"], check=True)\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": ["target_name", "certification_id", "port_id"]}], "writes": [{"table": "electric_vehicle_stats", "columns": ["target_name", "certification_id", "port_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO stg.cart_item_full SELECT gender, show_id, group_name, gas_fee FROM engineer_skills WHERE gender > 40\");\n", "labels": {"reads": [{"table": "engineer_skills", "columns": ["gender", "show_id", "group_name", "gas_fee"]}], "writes": [{"table": "stg.cart_item_full", "columns": ["gender", "show_id", "group_name", "gas_fee"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO dp_articles SELECT market_value, opponent_id FROM mappinglengths WHERE market_value > 462\");\n", "labels": {"reads": [{"table": "mappinglengths", "columns": ["market_value", "opponent_id"]}], "writes": [{"table": "dp_articles", "columns": ["market_value", "opponent_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT astronautid, cb_year FROM timber_sales\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"mart_shipments_full\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "timber_sales", "columns": ["astronautid", "cb_year"]}], "writes": [{"table": "mart_shipments_full", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO accelerator_compatible_browser (workshop_name, shop_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "accelerator_compatible_browser", "columns": ["workshop_name", "shop_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"pitstops\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "pitstops", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table researchers --columns volunteerhourid,chip_model --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "researchers", "columns": ["volunteerhourid", "chip_model"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM genetic_research\", conn)\ndf.to_sql(\"permit\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "genetic_research", "columns": null}], "writes": [{"table": "permit", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO organisations SELECT mappingname, environmental_impact, screen_mode FROM cuisine WHERE mappingname > 24\"\n", "labels": {"reads": [{"table": "cuisine", "columns": ["mappingname", "environmental_impact", "screen_mode"]}], "writes": [{"table": "organisations", "columns": ["mappingname", "environmental_impact", "screen_mode"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mobile_usage SELECT a.funding_source, b.outcome_code FROM sustainable_materials a JOIN financialwellbeing b ON a.exhibitions = b.exhibitions\"\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": null}, {"table": "financialwellbeing", "columns": null}], "writes": [{"table": "mobile_usage", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"rainfall_data\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"staff_roles\")\n", "labels": {"reads": [{"table": "rainfall_data", "columns": null}], "writes": [{"table": "staff_roles", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table movies --columns painting_name,ship_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "movies", "columns": ["painting_name", "ship_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO fish_farms SELECT roomname, transaction_type_code, satelliteid, access_date FROM temperaturerecords WHERE roomname > 64\"\n", "labels": {"reads": [{"table": "temperaturerecords", "columns": ["roomname", "transaction_type_code", "satelliteid", "access_date"]}], "writes": [{"table": "fish_farms", "columns": ["roomname", "transaction_type_code", "satelliteid", "access_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO space_telescopes (billingcountry, onscholarship) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "space_telescopes", "columns": ["billingcountry", "onscholarship"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 5;\nEOF\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": ["environmental_impact", "materialname", "uid"]}], "writes": [{"table": "broadband_plans", "columns": ["environmental_impact", "materialname", "uid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO safety_violations SELECT playerregion, card_id FROM ods.ods_users_daily WHERE playerregion > 231\");\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": ["playerregion", "card_id"]}], "writes": [{"table": "safety_violations", "columns": ["playerregion", "card_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO human_resources SELECT * FROM legacy\ncur.execute(\"SELECT item_sold, art_type FROM nutritionfacts LIMIT 272\")\n", "labels": {"reads": [{"table": "nutritionfacts", "columns": ["item_sold", "art_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"useracct\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "useracct", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"menu_engineering\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"excavation_sites\")\n", "labels": {"reads": [{"table": "menu_engineering", "columns": null}], "writes": [{"table": "excavation_sites", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT trader_id, vehicle_details FROM philadelphia_police_emergencies LIMIT 457\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": ["trader_id", "vehicle_details"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM hospital_visits\", conn)\ndf.to_sql(\"staff\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "hospital_visits", "columns": null}], "writes": [{"table": "staff", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO decentralized_applications SELECT a.statement_details, b.stop FROM stg.stg_users_daily a JOIN lenders b ON a.fuelconsumed = b.fuelconsumed\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.stg_users_daily", "columns": null}, {"table": "lenders", "columns": null}], "writes": [{"table": "decentralized_applications", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM jobs\"\n", "labels": {"reads": [{"table": "jobs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"students_enrollment\").where(\"dt = current_date()\").writeTo(\"financialwellbeing\").append()\n", "labels": {"reads": [{"table": "students_enrollment", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"streams\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "streams", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"military_aircraft_maintenance\").where(\"dt = current_date()\").writeTo(\"carbon_sequestration\").append()\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": null}], "writes": [{"table": "carbon_sequestration", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sales_2\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"studies\")\n", "labels": {"reads": [{"table": "sales_2", "columns": null}], "writes": [{"table": "studies", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 261;\nSQL\n", "labels": {"reads": [{"table": "support_groups", "columns": ["aircraft_id", "cause"]}, {"table": "courses", "columns": ["author_or_editor", "mailing_date"]}], "writes": [{"table": "virtual_tour_stats", "columns": ["author_or_editor", "mailing_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT document_type_name, billing_country FROM community_events\", engine)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"rural_resources\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "community_events", "columns": ["document_type_name", "billing_country"]}], "writes": [{"table": "rural_resources", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"members\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"marine_life_research\")\n", "labels": {"reads": [{"table": "members", "columns": null}], "writes": [{"table": "marine_life_research", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO certifications SELECT attraction_id, product_name FROM culturalevents WHERE attraction_id > 66\"], check=True)\n", "labels": {"reads": [{"table": "culturalevents", "columns": ["attraction_id", "product_name"]}], "writes": [{"table": "certifications", "columns": ["attraction_id", "product_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO shipments SELECT courtid, activity_id, zipcode FROM parity_violations WHERE courtid > 173\"\n", "labels": {"reads": [{"table": "parity_violations", "columns": ["courtid", "activity_id", "zipcode"]}], "writes": [{"table": "shipments", "columns": ["courtid", "activity_id", "zipcode"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"procedures\")\nsrc.write.insertInto(\"stg.cart_item_full\", overwrite=True)\n", "labels": {"reads": [{"table": "procedures", "columns": null}], "writes": [{"table": "stg.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 390;\nEOF\n", "labels": {"reads": [{"table": "clothingitems", "columns": ["total_amount", "price_in_dollars", "albumname"]}], "writes": [{"table": "inclusive_housing", "columns": ["total_amount", "price_in_dollars", "albumname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_source(ctx, \"certifications\")\nupsert_to_store(df, \"strainlabresults\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "certifications", "columns": null}], "writes": [{"table": "strainlabresults", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT menucategory, policy_number FROM stg.stg_shipments_hourly LIMIT 95\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO countries SELECT total_cost, clinic_type FROM mealtypes WHERE total_cost > 3\")\n", "labels": {"reads": [{"table": "stg.stg_shipments_hourly", "columns": ["menucategory", "policy_number"]}, {"table": "mealtypes", "columns": ["total_cost", "clinic_type"]}], "writes": [{"table": "countries", "columns": ["total_cost", "clinic_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dwd.dwd_payments_di SELECT donator_name, build_date, destination FROM spacemissions WHERE donator_name > 28\"\n", "labels": {"reads": [{"table": "spacemissions", "columns": ["donator_name", "build_date", "destination"]}], "writes": [{"table": "dwd.dwd_payments_di", "columns": ["donator_name", "build_date", "destination"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"textileworkers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"militarydrones\")\n", "labels": {"reads": [{"table": "textileworkers", "columns": null}], "writes": [{"table": "militarydrones", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO regular_order_products (campaign_id, spending) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "regular_order_products", "columns": ["campaign_id", "spending"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"explainable_ai\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "explainable_ai", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table camera_lens --columns length_feet,sponsor_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "camera_lens", "columns": ["length_feet", "sponsor_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO invoice SELECT initiative_name, testtypeid, is_false FROM chemicals_annual WHERE initiative_name > 338\"\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": ["initiative_name", "testtypeid", "is_false"]}], "writes": [{"table": "invoice", "columns": ["initiative_name", "testtypeid", "is_false"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model disaster_response depends on bi_products\ndbt run -s disaster_response --vars '{\"source_table\":\"bi_products\"}'\n", "labels": {"reads": [{"table": "bi_products", "columns": null}], "writes": [{"table": "disaster_response", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_incident_end, fine FROM temperatureanomalies LIMIT 207\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "temperatureanomalies", "columns": ["date_incident_end", "fine"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"labor_unions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "labor_unions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT star_rating_code, end_station_name FROM ref_service_types\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"emergencies\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ref_service_types", "columns": ["star_rating_code", "end_station_name"]}], "writes": [{"table": "emergencies", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM circular_economy_initiatives\"\n", "labels": {"reads": [{"table": "circular_economy_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fair_trade_suppliers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"iot_sensors\")\n", "labels": {"reads": [{"table": "fair_trade_suppliers", "columns": null}], "writes": [{"table": "iot_sensors", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table innovation_grants --target-dir /tmp/land\n", "labels": {"reads": [{"table": "innovation_grants", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO viewership SELECT courtid, hours_served FROM excavation WHERE courtid > 227\")\n", "labels": {"reads": [{"table": "excavation", "columns": ["courtid", "hours_served"]}], "writes": [{"table": "viewership", "columns": ["courtid", "hours_served"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM clothingitems\", conn)\ndf.to_sql(\"swimmer\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "clothingitems", "columns": null}], "writes": [{"table": "swimmer", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 346;\nEOF\n", "labels": {"reads": [{"table": "dwd_payments_delta", "columns": ["issue_id", "app_id"]}], "writes": [{"table": "military_innovation", "columns": ["issue_id", "app_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO english_premier_league SELECT a.campus, b.film_id FROM nutrition_facts a JOIN tweets b ON a.customer_event_id = b.customer_event_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "nutrition_facts", "columns": null}, {"table": "tweets", "columns": null}], "writes": [{"table": "english_premier_league", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO fleet_management SELECT strainname, test_result, club_name FROM education WHERE strainname > 339\")\n", "labels": {"reads": [{"table": "education", "columns": ["strainname", "test_result", "club_name"]}], "writes": [{"table": "fleet_management", "columns": ["strainname", "test_result", "club_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 295;\nEOF\n", "labels": {"reads": [{"table": "decentralized_applications", "columns": ["amount_donated", "chair_name"]}], "writes": [{"table": "fault_log_parts", "columns": ["amount_donated", "chair_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_products_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cultural_competency_program\")\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": null}], "writes": [{"table": "cultural_competency_program", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO mediterranean_salinity SELECT technique, monthly_rental, donor_state, problem_id FROM grad_students WHERE technique > 302\"\n", "labels": {"reads": [{"table": "grad_students", "columns": ["technique", "monthly_rental", "donor_state", "problem_id"]}], "writes": [{"table": "mediterranean_salinity", "columns": ["technique", "monthly_rental", "donor_state", "problem_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.quarter > 191).all()\n# src table: checking\nengine.execute(\"INSERT INTO public_transportation_sydney SELECT * FROM checking\")\n", "labels": {"reads": [{"table": "checking", "columns": null}], "writes": [{"table": "public_transportation_sydney", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM public.forest_stats\"\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"inst\");\ndf.write().mode(\"overwrite\").saveAsTable(\"satellites_in_orbit\");\n", "labels": {"reads": [{"table": "inst", "columns": null}], "writes": [{"table": "satellites_in_orbit", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO indian_ocean_fishingvessels SELECT waste_id, mealid, emp_lname, hireyear FROM trainingprograms WHERE waste_id > 411\"\n", "labels": {"reads": [{"table": "trainingprograms", "columns": ["waste_id", "mealid", "emp_lname", "hireyear"]}], "writes": [{"table": "indian_ocean_fishingvessels", "columns": ["waste_id", "mealid", "emp_lname", "hireyear"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO parking_fines SELECT publish_date, booking_status_code, mission_count, stock FROM rural.bus_trips WHERE publish_date > 278\"\n", "labels": {"reads": [{"table": "rural.bus_trips", "columns": ["publish_date", "booking_status_code", "mission_count", "stock"]}], "writes": [{"table": "parking_fines", "columns": ["publish_date", "booking_status_code", "mission_count", "stock"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO vessel SELECT mission_id, profits_billion, restaurant_id, meal_id FROM marine_mammals WHERE mission_id > 169\");\n", "labels": {"reads": [{"table": "marine_mammals", "columns": ["mission_id", "profits_billion", "restaurant_id", "meal_id"]}], "writes": [{"table": "vessel", "columns": ["mission_id", "profits_billion", "restaurant_id", "meal_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO chemicalbatches (precedent_id, focal_length_mm) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "chemicalbatches", "columns": ["precedent_id", "focal_length_mm"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"fish_purchases\").where(\"dt = current_date()\").writeTo(\"film_category\").append()\n", "labels": {"reads": [{"table": "fish_purchases", "columns": null}], "writes": [{"table": "film_category", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bridgerainfall SELECT appointment_time, retailer_id, highscore FROM flu_shots WHERE appointment_time > 390\"\n", "labels": {"reads": [{"table": "flu_shots", "columns": ["appointment_time", "retailer_id", "highscore"]}], "writes": [{"table": "bridgerainfall", "columns": ["appointment_time", "retailer_id", "highscore"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO workshops SELECT artworkyear, views FROM experts WHERE artworkyear > 262\");\n", "labels": {"reads": [{"table": "experts", "columns": ["artworkyear", "views"]}], "writes": [{"table": "workshops", "columns": ["artworkyear", "views"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM useracct\"\n", "labels": {"reads": [{"table": "useracct", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM co_ownership\"\n", "labels": {"reads": [{"table": "co_ownership", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_contracts_v2\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tb_cases\")\n", "labels": {"reads": [{"table": "defense_contracts_v2", "columns": null}], "writes": [{"table": "tb_cases", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO ai_safety SELECT a.delivery_status, b.dorm_name FROM securityincidents a JOIN mentalhealthscores b ON a.calories = b.calories\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "securityincidents", "columns": null}, {"table": "mentalhealthscores", "columns": null}], "writes": [{"table": "ai_safety", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table development_hours --columns programid,anomaly --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "development_hours", "columns": ["programid", "anomaly"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_payments_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.mart_payments_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vehicle_registrations SELECT a.is_dessert, b.review_id FROM arctic_research a JOIN department_publications b ON a.policy_holder_id = b.policy_holder_id\"\n", "labels": {"reads": [{"table": "arctic_research", "columns": null}, {"table": "department_publications", "columns": null}], "writes": [{"table": "vehicle_registrations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT billingcountry, date_of_publication FROM restorative_justice_sentences LIMIT 296\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "restorative_justice_sentences", "columns": ["billingcountry", "date_of_publication"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO construction_union (sqft, satellite_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "construction_union", "columns": ["sqft", "satellite_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO bi.bi_sessions_hourly SELECT active_to_date, releaseyear FROM satisfaction WHERE active_to_date > 276\"\n", "labels": {"reads": [{"table": "satisfaction", "columns": ["active_to_date", "releaseyear"]}], "writes": [{"table": "bi.bi_sessions_hourly", "columns": ["active_to_date", "releaseyear"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_interests\").toPandas()\ndf[[\"journal_id\", \"hotel_chain_name\"]].to_sql(\"employees\", engine, index=False)\n", "labels": {"reads": [{"table": "user_interests", "columns": null}], "writes": [{"table": "employees", "columns": ["journal_id", "hotel_chain_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO criminal_justice_reform_initiatives SELECT avg_speed, network_name FROM music_events WHERE avg_speed > 368\")\n", "labels": {"reads": [{"table": "music_events", "columns": ["avg_speed", "network_name"]}], "writes": [{"table": "criminal_justice_reform_initiatives", "columns": ["avg_speed", "network_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO volunteer_hours SELECT production_usage, name_full, check_in_id FROM network_infrastructure WHERE production_usage > 112\");\n", "labels": {"reads": [{"table": "network_infrastructure", "columns": ["production_usage", "name_full", "check_in_id"]}], "writes": [{"table": "volunteer_hours", "columns": ["production_usage", "name_full", "check_in_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"machine_emissions\")\nsrc.write.insertInto(\"expenses\", overwrite=True)\n", "labels": {"reads": [{"table": "machine_emissions", "columns": null}], "writes": [{"table": "expenses", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT min_age, meal_name FROM biosensors\", engine)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"ods.vendors_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "biosensors", "columns": ["min_age", "meal_name"]}], "writes": [{"table": "ods.vendors_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"virtual_tour_stats\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"humanitarianassistanceoperations\")\n", "labels": {"reads": [{"table": "virtual_tour_stats", "columns": null}], "writes": [{"table": "humanitarianassistanceoperations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO workout SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart_refunds\"\n", "labels": {"reads": [{"table": "mart_refunds", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO apartment_bookings SELECT lastdonationdate, shariah_compliant_investment_amount FROM languages WHERE lastdonationdate > 18\");\n", "labels": {"reads": [{"table": "languages", "columns": ["lastdonationdate", "shariah_compliant_investment_amount"]}], "writes": [{"table": "apartment_bookings", "columns": ["lastdonationdate", "shariah_compliant_investment_amount"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO farm_competition SELECT menuitem, lettergrade, max_page_size, title FROM ads.ads_refunds_hourly WHERE menuitem > 375\"\n", "labels": {"reads": [{"table": "ads.ads_refunds_hourly", "columns": ["menuitem", "lettergrade", "max_page_size", "title"]}], "writes": [{"table": "farm_competition", "columns": ["menuitem", "lettergrade", "max_page_size", "title"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 232;\nEOF\n", "labels": {"reads": [{"table": "country_renewable_energy", "columns": ["organisation_type_description", "clubname", "online_dispute_resolution", "played"]}], "writes": [{"table": "asteroids", "columns": ["organisation_type_description", "clubname", "online_dispute_resolution", "played"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM film_market_estimation\"\n", "labels": {"reads": [{"table": "film_market_estimation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO vessel_registry SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"communities\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_member_point_daily SELECT * FROM legacy\ncur.execute(\"SELECT home_team_three_point, wage FROM flights LIMIT 134\")\n", "labels": {"reads": [{"table": "flights", "columns": ["home_team_three_point", "wage"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 421;\nSQL\n", "labels": {"reads": [{"table": "seafoodsouthafricakenya", "columns": ["media_literacy_score", "assistingnurse"]}, {"table": "worker_scores", "columns": ["physical", "shippedcost"]}], "writes": [{"table": "expenditure", "columns": ["physical", "shippedcost"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO vessel SELECT student_details, plan_id, resolution FROM energy_efficiency_programs WHERE student_details > 435\")\n", "labels": {"reads": [{"table": "energy_efficiency_programs", "columns": ["student_details", "plan_id", "resolution"]}], "writes": [{"table": "vessel", "columns": ["student_details", "plan_id", "resolution"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO reservations SELECT * FROM legacy\ncur.execute(\"SELECT class, units_sold FROM policy_advocacy LIMIT 200\")\n", "labels": {"reads": [{"table": "policy_advocacy", "columns": ["class", "units_sold"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO volunteer_hours SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO gameplatforms SELECT a.local_authority, b.medicine_id FROM ref_shipping_agents a JOIN members b ON a.fda_approved = b.fda_approved\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ref_shipping_agents", "columns": null}, {"table": "members", "columns": null}], "writes": [{"table": "gameplatforms", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_events_daily SELECT wrestler_id, ad_id FROM genetics.experiments WHERE wrestler_id > 161\"\n", "labels": {"reads": [{"table": "genetics.experiments", "columns": ["wrestler_id", "ad_id"]}], "writes": [{"table": "ods.ods_events_daily", "columns": ["wrestler_id", "ad_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"user_video_view\")\nsrc.write.insertInto(\"space_missions\", overwrite=True)\n", "labels": {"reads": [{"table": "user_video_view", "columns": null}], "writes": [{"table": "space_missions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sustainable_building SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM zipcodes\", conn)\ndf.to_sql(\"brandrevenue\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "zipcodes", "columns": null}], "writes": [{"table": "brandrevenue", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT release_year, material FROM reverselogisticstransactions\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"complaints\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": ["release_year", "material"]}], "writes": [{"table": "complaints", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO game_sales SELECT a.flight_number, b.complaint_type_code FROM ads_payments_di a JOIN brandrevenue b ON a.route_short_name = b.route_short_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": null}, {"table": "brandrevenue", "columns": null}], "writes": [{"table": "game_sales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"water_conservation\")\nsrc.write.insertInto(\"financial_capability_id\", overwrite=True)\n", "labels": {"reads": [{"table": "water_conservation", "columns": null}], "writes": [{"table": "financial_capability_id", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dws.dws_orders_full\").where(\"dt = current_date()\").writeTo(\"socially_responsible_loans\").append()\n", "labels": {"reads": [{"table": "dws.dws_orders_full", "columns": null}], "writes": [{"table": "socially_responsible_loans", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"waste_generation\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"nasa_mars_program\")\n", "labels": {"reads": [{"table": "waste_generation", "columns": null}], "writes": [{"table": "nasa_mars_program", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO directors SELECT vehicle_model, heartrate, head_id, document_status_description FROM sustainablebrands WHERE vehicle_model > 167\")\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": ["vehicle_model", "heartrate", "head_id", "document_status_description"]}], "writes": [{"table": "directors", "columns": ["vehicle_model", "heartrate", "head_id", "document_status_description"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO management SELECT * FROM legacy\nspark.sql(\"INSERT INTO workout_sessions SELECT dribbling, cargo, deliveryaddress, gross_worldwide FROM programoutcomes WHERE dribbling > 253\")\n", "labels": {"reads": [{"table": "programoutcomes", "columns": ["dribbling", "cargo", "deliveryaddress", "gross_worldwide"]}], "writes": [{"table": "workout_sessions", "columns": ["dribbling", "cargo", "deliveryaddress", "gross_worldwide"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"diversification_projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "diversification_projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO patient SELECT gamegenre, crs_description, contractor_name, commanding_officer FROM customer_contact_channels WHERE gamegenre > 22\"\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": ["gamegenre", "crs_description", "contractor_name", "commanding_officer"]}], "writes": [{"table": "patient", "columns": ["gamegenre", "crs_description", "contractor_name", "commanding_officer"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.shipments_df SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"thefttypes\")\nsrc.write.insertInto(\"ai_for_social_good\", overwrite=True)\n", "labels": {"reads": [{"table": "thefttypes", "columns": null}], "writes": [{"table": "ai_for_social_good", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table agri_innovations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "agri_innovations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO civilcases SELECT employment_id, collection_id, publication_id, calories FROM dw.shipments_di WHERE employment_id > 287\"\n", "labels": {"reads": [{"table": "dw.shipments_di", "columns": ["employment_id", "collection_id", "publication_id", "calories"]}], "writes": [{"table": "civilcases", "columns": ["employment_id", "collection_id", "publication_id", "calories"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO list SELECT color, person_id, has_spf, timestamp FROM streams WHERE color > 3\");\n", "labels": {"reads": [{"table": "streams", "columns": ["color", "person_id", "has_spf", "timestamp"]}], "writes": [{"table": "list", "columns": ["color", "person_id", "has_spf", "timestamp"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO esports_teams SELECT bandmate, vessel FROM nba_games WHERE bandmate > 446\"\n", "labels": {"reads": [{"table": "nba_games", "columns": ["bandmate", "vessel"]}], "writes": [{"table": "esports_teams", "columns": ["bandmate", "vessel"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"evsales\").where(\"dt = current_date()\").writeTo(\"us_military_personnel\").append()\n", "labels": {"reads": [{"table": "evsales", "columns": null}], "writes": [{"table": "us_military_personnel", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO paris_real_estate SELECT meal_date, permit_id FROM recruiters WHERE meal_date > 130\"\n", "labels": {"reads": [{"table": "recruiters", "columns": ["meal_date", "permit_id"]}], "writes": [{"table": "paris_real_estate", "columns": ["meal_date", "permit_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table rent_arrears --columns co_owner_count,name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "rent_arrears", "columns": ["co_owner_count", "name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"violations\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "violations", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"military_technology_projects\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "military_technology_projects", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"malicious_activity\").where(\"dt = current_date()\").writeTo(\"socially_responsible_loans\").append()\n", "labels": {"reads": [{"table": "malicious_activity", "columns": null}], "writes": [{"table": "socially_responsible_loans", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 403;\nEOF\n", "labels": {"reads": [{"table": "healthcare", "columns": ["session_id", "eventattendance"]}], "writes": [{"table": "legislation", "columns": ["session_id", "eventattendance"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO songs SELECT label_id, system_id, num_volunteers, implementation_date FROM permit WHERE label_id > 40\"\n", "labels": {"reads": [{"table": "permit", "columns": ["label_id", "system_id", "num_volunteers", "implementation_date"]}], "writes": [{"table": "songs", "columns": ["label_id", "system_id", "num_volunteers", "implementation_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"stg.risk_score_di\")\nsrc.write.insertInto(\"infrastructureprojects\", overwrite=True)\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": null}], "writes": [{"table": "infrastructureprojects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"productivity\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "productivity", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO exhibitionattendance SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"asteroids\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_exposure_daily\")\n", "labels": {"reads": [{"table": "asteroids", "columns": null}], "writes": [{"table": "ads.ads_exposure_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"emergencies\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"divisions\")\n", "labels": {"reads": [{"table": "emergencies", "columns": null}], "writes": [{"table": "divisions", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"phishing_targets\")\nsrc.write.insertInto(\"medical_facilities\", overwrite=True)\n", "labels": {"reads": [{"table": "phishing_targets", "columns": null}], "writes": [{"table": "medical_facilities", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO ocean_health_monitor SELECT paintingid, ship_id, shipment_year, trader_id FROM firestations WHERE paintingid > 206\"\n", "labels": {"reads": [{"table": "firestations", "columns": ["paintingid", "ship_id", "shipment_year", "trader_id"]}], "writes": [{"table": "ocean_health_monitor", "columns": ["paintingid", "ship_id", "shipment_year", "trader_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"show\").where(\"dt = current_date()\").writeTo(\"employee_demographics\").append()\n", "labels": {"reads": [{"table": "show", "columns": null}], "writes": [{"table": "employee_demographics", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO eia_schedule SELECT base_id, ethnicity, value_points, year_founded FROM rural_infrastructure WHERE base_id > 80\"], check=True)\n", "labels": {"reads": [{"table": "rural_infrastructure", "columns": ["base_id", "ethnicity", "value_points", "year_founded"]}], "writes": [{"table": "eia_schedule", "columns": ["base_id", "ethnicity", "value_points", "year_founded"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO therapists SELECT a.incident_type_code, b.address_content FROM publications a JOIN catalogs b ON a.vol_id = b.vol_id\"\n", "labels": {"reads": [{"table": "publications", "columns": null}, {"table": "catalogs", "columns": null}], "writes": [{"table": "therapists", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mining_operation_data\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"facility\")\n", "labels": {"reads": [{"table": "mining_operation_data", "columns": null}], "writes": [{"table": "facility", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table clothing_brands --columns operationdate,pilot_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "clothing_brands", "columns": ["operationdate", "pilot_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO labor_cost SELECT productid, financially_capable, decoration_theme, oil_production_q4_2021 FROM ai_papers WHERE productid > 416\"\n", "labels": {"reads": [{"table": "ai_papers", "columns": ["productid", "financially_capable", "decoration_theme", "oil_production_q4_2021"]}], "writes": [{"table": "labor_cost", "columns": ["productid", "financially_capable", "decoration_theme", "oil_production_q4_2021"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO consumer_preference SELECT a.age_group_id, b.orgname FROM safetyincidents a JOIN ocean_floor b ON a.approach = b.approach\"\n", "labels": {"reads": [{"table": "safetyincidents", "columns": null}, {"table": "ocean_floor", "columns": null}], "writes": [{"table": "consumer_preference", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO attendees SELECT menu_category, sent_date, park_id, facility_id FROM salesperson WHERE menu_category > 363\");\n", "labels": {"reads": [{"table": "salesperson", "columns": ["menu_category", "sent_date", "park_id", "facility_id"]}], "writes": [{"table": "attendees", "columns": ["menu_category", "sent_date", "park_id", "facility_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM chemical_production_5\"\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT last_used_bus, hospital_name FROM ods.vendors_di LIMIT 110\")\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO user_likes SELECT location_text, job, trip_id FROM country_renewable_energy WHERE location_text > 298\")\n", "labels": {"reads": [{"table": "ods.vendors_di", "columns": ["last_used_bus", "hospital_name"]}, {"table": "country_renewable_energy", "columns": ["location_text", "job", "trip_id"]}], "writes": [{"table": "user_likes", "columns": ["location_text", "job", "trip_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT researcher, party_phone FROM mart.mart_payments_delta LIMIT 77\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "mart.mart_payments_delta", "columns": ["researcher", "party_phone"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ocean_depths (mental_health_score, hotel_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ocean_depths", "columns": ["mental_health_score", "hotel_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model contract_states depends on stg.stg_risk_score_df\ndbt build --select contract_states --vars '{\"source_table\":\"stg.stg_risk_score_df\"}'\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": null}], "writes": [{"table": "contract_states", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO lawyers (artpieceid, lender_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "lawyers", "columns": ["artpieceid", "lender_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO smartcontracts SELECT ai_adoption_date, channel_code, last_workout_date FROM pilot WHERE ai_adoption_date > 284\"\n", "labels": {"reads": [{"table": "pilot", "columns": ["ai_adoption_date", "channel_code", "last_workout_date"]}], "writes": [{"table": "smartcontracts", "columns": ["ai_adoption_date", "channel_code", "last_workout_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO criticalincidents SELECT player_id, organic FROM spacecraft_manufacturing WHERE player_id > 212\"\n", "labels": {"reads": [{"table": "spacecraft_manufacturing", "columns": ["player_id", "organic"]}], "writes": [{"table": "criticalincidents", "columns": ["player_id", "organic"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"flight_safety\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model bi_payments_df depends on reviews\ndbt build --select bi_payments_df --vars '{\"src\":\"reviews\"}'\n", "labels": {"reads": [{"table": "reviews", "columns": null}], "writes": [{"table": "bi_payments_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"runs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "runs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"properties\");\ndf.write().mode(\"overwrite\").saveAsTable(\"cinema\");\n", "labels": {"reads": [{"table": "properties", "columns": null}], "writes": [{"table": "cinema", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO security_incidents SELECT award, coalquantity FROM episodes WHERE award > 409\");\n", "labels": {"reads": [{"table": "episodes", "columns": ["award", "coalquantity"]}], "writes": [{"table": "security_incidents", "columns": ["award", "coalquantity"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO ticket_sales SELECT donor_state, menu_item_id FROM faculty_participates_in WHERE donor_state > 224\")\n", "labels": {"reads": [{"table": "faculty_participates_in", "columns": ["donor_state", "menu_item_id"]}], "writes": [{"table": "ticket_sales", "columns": ["donor_state", "menu_item_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT frequency, contract_address FROM inclusion_efforts\", engine)\nresult = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"renewable_power\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "inclusion_efforts", "columns": ["frequency", "contract_address"]}], "writes": [{"table": "renewable_power", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO divisions SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"manufacturing_processes\").where(\"dt = current_date()\").writeTo(\"ads.ads_device_log_di\").append()\n", "labels": {"reads": [{"table": "manufacturing_processes", "columns": null}], "writes": [{"table": "ads.ads_device_log_di", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT marketing_region_descriptrion, building_description FROM musical LIMIT 129\")\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO legislation SELECT rid, date_became_customer, production_budget, num_cases FROM countryintelligenceops WHERE rid > 170\")\n", "labels": {"reads": [{"table": "musical", "columns": ["marketing_region_descriptrion", "building_description"]}, {"table": "countryintelligenceops", "columns": ["rid", "date_became_customer", "production_budget", "num_cases"]}], "writes": [{"table": "legislation", "columns": ["rid", "date_became_customer", "production_budget", "num_cases"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods_shipments_df SELECT a.gname, b.project_number FROM heritage_sites_3 a JOIN performance_scores b ON a.sentence_length = b.sentence_length\"\n", "labels": {"reads": [{"table": "heritage_sites_3", "columns": null}, {"table": "performance_scores", "columns": null}], "writes": [{"table": "ods_shipments_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"culturalpractices\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "culturalpractices", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM wholesale_orders\", conn)\ndf.to_sql(\"parity_violations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": null}], "writes": [{"table": "parity_violations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.refunds_daily SELECT healthequitymetricscore, catalog_entry_name, budget_type_code FROM esportsteamsafrica WHERE healthequitymetricscore > 245\"\n", "labels": {"reads": [{"table": "esportsteamsafrica", "columns": ["healthequitymetricscore", "catalog_entry_name", "budget_type_code"]}], "writes": [{"table": "bi.refunds_daily", "columns": ["healthequitymetricscore", "catalog_entry_name", "budget_type_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT blockcode, medium FROM co2_emissions\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"greenbuildings\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "co2_emissions", "columns": ["blockcode", "medium"]}], "writes": [{"table": "greenbuildings", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO peacekeepingmissions SELECT treatment, funding_round_id, dockingid, well_type FROM problem_log WHERE treatment > 367\")\n", "labels": {"reads": [{"table": "problem_log", "columns": ["treatment", "funding_round_id", "dockingid", "well_type"]}], "writes": [{"table": "peacekeepingmissions", "columns": ["treatment", "funding_round_id", "dockingid", "well_type"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT fairtrade, meter_300 FROM e_scooter_trips\", engine)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"stg_payments_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": ["fairtrade", "meter_300"]}], "writes": [{"table": "stg_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.total_spent > 353).all()\n# src table: highways\nengine.execute(\"INSERT INTO settlements SELECT * FROM highways\")\n", "labels": {"reads": [{"table": "highways", "columns": null}], "writes": [{"table": "settlements", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model coowners depends on ads.orders_daily\ndbt run --models coowners --vars '{\"src\":\"ads.orders_daily\"}'\n", "labels": {"reads": [{"table": "ads.orders_daily", "columns": null}], "writes": [{"table": "coowners", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.recordid > 88).all()\n# src table: casesbyyear\nengine.execute(\"INSERT INTO ods.shipments_df SELECT * FROM casesbyyear\")\n", "labels": {"reads": [{"table": "casesbyyear", "columns": null}], "writes": [{"table": "ods.shipments_df", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 411;\nEOF\n", "labels": {"reads": [{"table": "student_mental_health", "columns": ["item_sold", "ad_id", "address_type_code"]}], "writes": [{"table": "assessment_notes", "columns": ["item_sold", "ad_id", "address_type_code"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM communities\"\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT number_of_platforms, date_contact_to FROM ref_locations\", engine)\nimport logging\ndf.to_sql(\"city_properties\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ref_locations", "columns": ["number_of_platforms", "date_contact_to"]}], "writes": [{"table": "city_properties", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"educators\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"water_distribution\")\n", "labels": {"reads": [{"table": "educators", "columns": null}], "writes": [{"table": "water_distribution", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO ref_colors SELECT participant_type_code, genderid, implementation_year FROM soilanalysis WHERE participant_type_code > 153\");\n", "labels": {"reads": [{"table": "soilanalysis", "columns": ["participant_type_code", "genderid", "implementation_year"]}], "writes": [{"table": "ref_colors", "columns": ["participant_type_code", "genderid", "implementation_year"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 315;\nEOF\n", "labels": {"reads": [{"table": "ods.ods_events_daily", "columns": ["raceid", "incident_name"]}], "writes": [{"table": "mart.shipments_delta", "columns": ["raceid", "incident_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO county_public_safety SELECT opening_hours, time_of_day FROM outcomes WHERE opening_hours > 325\"\n", "labels": {"reads": [{"table": "outcomes", "columns": ["opening_hours", "time_of_day"]}], "writes": [{"table": "county_public_safety", "columns": ["opening_hours", "time_of_day"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"irrigation_systems\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"visitor_exhibition\")\n", "labels": {"reads": [{"table": "irrigation_systems", "columns": null}], "writes": [{"table": "visitor_exhibition", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO fault_log SELECT domestic_passengers, field_name, meter_300 FROM dwd.dwd_campaigns_df WHERE domestic_passengers > 44\"\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": ["domestic_passengers", "field_name", "meter_300"]}], "writes": [{"table": "fault_log", "columns": ["domestic_passengers", "field_name", "meter_300"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM leed_buildings\"\n", "labels": {"reads": [{"table": "leed_buildings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO stations SELECT aircraft_id, rental_date, gtype, mission_name FROM stg.events_hourly WHERE aircraft_id > 129\")\n", "labels": {"reads": [{"table": "stg.events_hourly", "columns": ["aircraft_id", "rental_date", "gtype", "mission_name"]}], "writes": [{"table": "stations", "columns": ["aircraft_id", "rental_date", "gtype", "mission_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM pharmasales\"\n", "labels": {"reads": [{"table": "pharmasales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model road_construction depends on defense_projects\ndbt run --models road_construction --vars '{\"src\":\"defense_projects\"}'\n", "labels": {"reads": [{"table": "defense_projects", "columns": null}], "writes": [{"table": "road_construction", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"activities\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "activities", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_member_point_df\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"all_programs\")\n", "labels": {"reads": [{"table": "mart.mart_member_point_df", "columns": null}], "writes": [{"table": "all_programs", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"roads\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"cosmetic_formula\")\n", "labels": {"reads": [{"table": "roads", "columns": null}], "writes": [{"table": "cosmetic_formula", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"checking\").toPandas()\ndf[[\"num_sustainable_materials\", \"firstname\"]].to_sql(\"rebounds\", engine, index=False)\n", "labels": {"reads": [{"table": "checking", "columns": null}], "writes": [{"table": "rebounds", "columns": ["num_sustainable_materials", "firstname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"attack_outcomes\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "attack_outcomes", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table artist_data --columns extraction_state,cuisine_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "artist_data", "columns": ["extraction_state", "cuisine_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO fish_biomass SELECT invested, individual_name, users_engaged, gamegenre FROM stg.stg_users_di WHERE invested > 492\")\n", "labels": {"reads": [{"table": "stg.stg_users_di", "columns": ["invested", "individual_name", "users_engaged", "gamegenre"]}], "writes": [{"table": "fish_biomass", "columns": ["invested", "individual_name", "users_engaged", "gamegenre"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"restaurants\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tickets\")\n", "labels": {"reads": [{"table": "restaurants", "columns": null}], "writes": [{"table": "tickets", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM artcollection\", conn)\ndf.to_sql(\"virtual_tourism\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "artcollection", "columns": null}], "writes": [{"table": "virtual_tourism", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"totalenergyproduction\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"autonomous_testing\")\n", "labels": {"reads": [{"table": "totalenergyproduction", "columns": null}], "writes": [{"table": "autonomous_testing", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO whale_sightings SELECT policy_count, followers, releasedate FROM bi.clicks_df WHERE policy_count > 40\");\n", "labels": {"reads": [{"table": "bi.clicks_df", "columns": ["policy_count", "followers", "releasedate"]}], "writes": [{"table": "whale_sightings", "columns": ["policy_count", "followers", "releasedate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO legislation (festival_id, meter_300) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "legislation", "columns": ["festival_id", "meter_300"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO food_justice SELECT total_budget_percent_budgeted, shipped_date, assessmentname FROM marketing_budgets WHERE total_budget_percent_budgeted > 321\")\n", "labels": {"reads": [{"table": "marketing_budgets", "columns": ["total_budget_percent_budgeted", "shipped_date", "assessmentname"]}], "writes": [{"table": "food_justice", "columns": ["total_budget_percent_budgeted", "shipped_date", "assessmentname"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO salesperson SELECT a.zone_id, b.response_received_date FROM course_authors_and_tutors a JOIN product_reviews b ON a.race_ethnicity = b.race_ethnicity\"\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": null}, {"table": "product_reviews", "columns": null}], "writes": [{"table": "salesperson", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO innovation_metrics SELECT * FROM legacy\nspark.sql(\"INSERT INTO shariah_compliant_loans SELECT gname, funder, violationtype FROM bi.bi_inventory_di WHERE gname > 368\")\n", "labels": {"reads": [{"table": "bi.bi_inventory_di", "columns": ["gname", "funder", "violationtype"]}], "writes": [{"table": "shariah_compliant_loans", "columns": ["gname", "funder", "violationtype"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dams SELECT * FROM legacy\nspark.sql(\"INSERT INTO marketing_budgets SELECT hospitalid, biome_id FROM cloud_issues WHERE hospitalid > 440\")\n", "labels": {"reads": [{"table": "cloud_issues", "columns": ["hospitalid", "biome_id"]}], "writes": [{"table": "marketing_budgets", "columns": ["hospitalid", "biome_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO city_waste_generation SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO explainableai SELECT tripdatetime, visitor_name, manufacturerid FROM productsafety WHERE tripdatetime > 117\"\n", "labels": {"reads": [{"table": "productsafety", "columns": ["tripdatetime", "visitor_name", "manufacturerid"]}], "writes": [{"table": "explainableai", "columns": ["tripdatetime", "visitor_name", "manufacturerid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"budget_allocations\")\nsrc.write.insertInto(\"maintenance\", overwrite=True)\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "maintenance", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO dwd.dwd_campaigns_df SELECT mineid, views, consumer_id, hosts FROM dw.dw_events_di WHERE mineid > 54\"\n", "labels": {"reads": [{"table": "dw.dw_events_di", "columns": ["mineid", "views", "consumer_id", "hosts"]}], "writes": [{"table": "dwd.dwd_campaigns_df", "columns": ["mineid", "views", "consumer_id", "hosts"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO intelligence_agents SELECT coach_id, song_release_year FROM lead_mines WHERE coach_id > 279\");\n", "labels": {"reads": [{"table": "lead_mines", "columns": ["coach_id", "song_release_year"]}], "writes": [{"table": "intelligence_agents", "columns": ["coach_id", "song_release_year"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table pollution_initiatives --columns day_number,screening_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "pollution_initiatives", "columns": ["day_number", "screening_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"artsales\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "artsales", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM test_drives\"\n", "labels": {"reads": [{"table": "test_drives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO match SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO sectors SELECT fault_description, water_consumption FROM course_authors_and_tutors WHERE fault_description > 131\")\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": ["fault_description", "water_consumption"]}], "writes": [{"table": "sectors", "columns": ["fault_description", "water_consumption"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT num_students, tournament_id FROM mart.risk_score_df LIMIT 245\")\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO dw_vendors_di SELECT daily_sales, jobtitle FROM restaurant WHERE daily_sales > 415\")\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": ["num_students", "tournament_id"]}, {"table": "restaurant", "columns": ["daily_sales", "jobtitle"]}], "writes": [{"table": "dw_vendors_di", "columns": ["daily_sales", "jobtitle"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 499;\nEOF\n", "labels": {"reads": [{"table": "fan_purchases", "columns": ["recipient_id", "dish_name"]}], "writes": [{"table": "vessel_performance", "columns": ["recipient_id", "dish_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO tweets SELECT pollutant_type, target_id, endtime, heritage_site_id FROM victims WHERE pollutant_type > 366\")\n", "labels": {"reads": [{"table": "victims", "columns": ["pollutant_type", "target_id", "endtime", "heritage_site_id"]}], "writes": [{"table": "tweets", "columns": ["pollutant_type", "target_id", "endtime", "heritage_site_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dws.events SELECT materialtype, garment_name, pipeline_name, end_station_id FROM agricultural_innovations WHERE materialtype > 298\"\n", "labels": {"reads": [{"table": "agricultural_innovations", "columns": ["materialtype", "garment_name", "pipeline_name", "end_station_id"]}], "writes": [{"table": "dws.events", "columns": ["materialtype", "garment_name", "pipeline_name", "end_station_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 71;\nEOF\n", "labels": {"reads": [{"table": "donationprograms", "columns": ["reign", "continent_id", "budget_allocated", "item_size"]}], "writes": [{"table": "deep_sea_expeditions", "columns": ["reign", "continent_id", "budget_allocated", "item_size"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_acidity\").toPandas()\ndf[[\"char_cells\", \"strainid\"]].to_sql(\"public.developers\", engine, index=False)\n", "labels": {"reads": [{"table": "ocean_acidity", "columns": null}], "writes": [{"table": "public.developers", "columns": ["char_cells", "strainid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO bi.events_delta SELECT omim, casestatus FROM expensive_space_missions WHERE omim > 421\"\n", "labels": {"reads": [{"table": "expensive_space_missions", "columns": ["omim", "casestatus"]}], "writes": [{"table": "bi.events_delta", "columns": ["omim", "casestatus"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO labor_productivity SELECT innovation, frameworkcountry FROM sales_region WHERE innovation > 58\")\n", "labels": {"reads": [{"table": "sales_region", "columns": ["innovation", "frameworkcountry"]}], "writes": [{"table": "labor_productivity", "columns": ["innovation", "frameworkcountry"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ods.ods_users_daily (cruelty_free, review_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ods.ods_users_daily", "columns": ["cruelty_free", "review_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"clothingitems\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"editor\")\n", "labels": {"reads": [{"table": "clothingitems", "columns": null}], "writes": [{"table": "editor", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ods_payments_delta\").where(\"dt = current_date()\").writeTo(\"ocean_species\").append()\n", "labels": {"reads": [{"table": "ods_payments_delta", "columns": null}], "writes": [{"table": "ocean_species", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"browser\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.stg_shipments_hourly\")\n", "labels": {"reads": [{"table": "browser", "columns": null}], "writes": [{"table": "stg.stg_shipments_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.worker > 432).all()\n# src table: permian_basin\nengine.execute(\"INSERT INTO model_fairness SELECT * FROM permian_basin\")\n", "labels": {"reads": [{"table": "permian_basin", "columns": null}], "writes": [{"table": "model_fairness", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT shipment_id, used_kb FROM waste_generation\", engine)\nresult = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"employees\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "waste_generation", "columns": ["shipment_id", "used_kb"]}], "writes": [{"table": "employees", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table satellite_missions_large --target-dir /tmp/land\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM assets_frameworks\"\n", "labels": {"reads": [{"table": "assets_frameworks", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO incidents_by_month (sales_amount, impressions) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "incidents_by_month", "columns": ["sales_amount", "impressions"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table landfill_capacity --target-dir /tmp/land\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_areas\").toPandas()\ndf[[\"circuitid\", \"issue_count\"]].to_sql(\"passenger_trips\", engine, index=False)\n", "labels": {"reads": [{"table": "rural_areas", "columns": null}], "writes": [{"table": "passenger_trips", "columns": ["circuitid", "issue_count"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"open_pedagogy\");\ndf.write().mode(\"overwrite\").saveAsTable(\"carbon_footprint\");\n", "labels": {"reads": [{"table": "open_pedagogy", "columns": null}], "writes": [{"table": "carbon_footprint", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO regulatory_frameworks SELECT a.railway_id, b.visit_id FROM zipcodes a JOIN investment_accounts b ON a.donortype = b.donortype\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "zipcodes", "columns": null}, {"table": "investment_accounts", "columns": null}], "writes": [{"table": "regulatory_frameworks", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO culturalcompetencytrainings SELECT mental_health_score, museumname, last_updated FROM euroavev WHERE mental_health_score > 473\"\n", "labels": {"reads": [{"table": "euroavev", "columns": ["mental_health_score", "museumname", "last_updated"]}], "writes": [{"table": "culturalcompetencytrainings", "columns": ["mental_health_score", "museumname", "last_updated"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO rural_feeder_roads SELECT a.effective_date, b.num_virtual_tours FROM salary a JOIN festivals b ON a.supplierid = b.supplierid\"\n", "labels": {"reads": [{"table": "salary", "columns": null}, {"table": "festivals", "columns": null}], "writes": [{"table": "rural_feeder_roads", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO game_sessions SELECT investment, fueldate, bandmateid, sport_id FROM aquaticfarm WHERE investment > 5\"\n", "labels": {"reads": [{"table": "aquaticfarm", "columns": ["investment", "fueldate", "bandmateid", "sport_id"]}], "writes": [{"table": "game_sessions", "columns": ["investment", "fueldate", "bandmateid", "sport_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO artpieces SELECT program_type, serve_id, evaluated_for_fairness FROM bi.bi_vendors_di WHERE program_type > 74\"\n", "labels": {"reads": [{"table": "bi.bi_vendors_di", "columns": ["program_type", "serve_id", "evaluated_for_fairness"]}], "writes": [{"table": "artpieces", "columns": ["program_type", "serve_id", "evaluated_for_fairness"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ref_colors SELECT game_count, payment_date, num_workers, pid FROM daily_transaction_volume WHERE game_count > 353\"\n", "labels": {"reads": [{"table": "daily_transaction_volume", "columns": ["game_count", "payment_date", "num_workers", "pid"]}], "writes": [{"table": "ref_colors", "columns": ["game_count", "payment_date", "num_workers", "pid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dwd_sessions_hourly\"\n", "labels": {"reads": [{"table": "dwd_sessions_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"bridges\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"deliveryaddresses\")\n", "labels": {"reads": [{"table": "bridges", "columns": null}], "writes": [{"table": "deliveryaddresses", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"acidification_data\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "acidification_data", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT policy_description, visit_year FROM satellites LIMIT 241\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO hosting_city SELECT bike_id, dish_name, crop_type, attendeename FROM mobile_usage WHERE bike_id > 48\")\n", "labels": {"reads": [{"table": "satellites", "columns": ["policy_description", "visit_year"]}, {"table": "mobile_usage", "columns": ["bike_id", "dish_name", "crop_type", "attendeename"]}], "writes": [{"table": "hosting_city", "columns": ["bike_id", "dish_name", "crop_type", "attendeename"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_salinity\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.cart_item_full SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthparityscores\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"hr.employees\")\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": null}], "writes": [{"table": "hr.employees", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 182;\nEOF\n", "labels": {"reads": [{"table": "features", "columns": ["domestic_passengers", "document_status_code", "postalcode"]}], "writes": [{"table": "safety_testing", "columns": ["domestic_passengers", "document_status_code", "postalcode"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT asset_model, providerid FROM song\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"movies\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "song", "columns": ["asset_model", "providerid"]}], "writes": [{"table": "movies", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table climate_adaptation --columns affiliation,mountain_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "climate_adaptation", "columns": ["affiliation", "mountain_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO supplychainemployees (founder_race, accident_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "supplychainemployees", "columns": ["founder_race", "accident_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"global_tournament\")\nsrc.write.insertInto(\"document_types\", overwrite=True)\n", "labels": {"reads": [{"table": "global_tournament", "columns": null}], "writes": [{"table": "document_types", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO ods.ods_coupon_use_di SELECT therapist_id, amenity_name, employees, class_section FROM game_scores WHERE therapist_id > 388\"\n", "labels": {"reads": [{"table": "game_scores", "columns": ["therapist_id", "amenity_name", "employees", "class_section"]}], "writes": [{"table": "ods.ods_coupon_use_di", "columns": ["therapist_id", "amenity_name", "employees", "class_section"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ca_menu_items SELECT length_meters, environmental_impact, school_id FROM philadelphia_police_emergencies WHERE length_meters > 3\"\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": ["length_meters", "environmental_impact", "school_id"]}], "writes": [{"table": "ca_menu_items", "columns": ["length_meters", "environmental_impact", "school_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO climatedata SELECT a.oil_production_q4_2021, b.hourid FROM landfillcapacitybycountry a JOIN innovation_grants b ON a.sessionid = b.sessionid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": null}, {"table": "innovation_grants", "columns": null}], "writes": [{"table": "climatedata", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT enrollment, asset_details FROM cyber_incidents LIMIT 98\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO dw_vendors_di SELECT sponsor_name, agegroup, duration, co2_offset_amount FROM stg.member_point_df WHERE sponsor_name > 176\")\n", "labels": {"reads": [{"table": "cyber_incidents", "columns": ["enrollment", "asset_details"]}, {"table": "stg.member_point_df", "columns": ["sponsor_name", "agegroup", "duration", "co2_offset_amount"]}], "writes": [{"table": "dw_vendors_di", "columns": ["sponsor_name", "agegroup", "duration", "co2_offset_amount"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"league\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "league", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"navalvessels\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"culturalcompetencytraining\")\n", "labels": {"reads": [{"table": "navalvessels", "columns": null}], "writes": [{"table": "culturalcompetencytraining", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vessels SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO drills SELECT outcome_code, mineral FROM extraction_methods WHERE outcome_code > 232\"\n", "labels": {"reads": [{"table": "extraction_methods", "columns": ["outcome_code", "mineral"]}], "writes": [{"table": "drills", "columns": ["outcome_code", "mineral"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM provinces\", conn)\ndf.to_sql(\"bi.refunds_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "provinces", "columns": null}], "writes": [{"table": "bi.refunds_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 194;\nSQL\n", "labels": {"reads": [{"table": "legal_aid_organizations", "columns": ["dance_form", "issue_count"]}, {"table": "record", "columns": ["engagement_date", "booked_amount"]}], "writes": [{"table": "carbon_footprint", "columns": ["engagement_date", "booked_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO flu_cases SELECT leadershiptraining, coal_reserve_remaining FROM store WHERE leadershiptraining > 23\")\n", "labels": {"reads": [{"table": "store", "columns": ["leadershiptraining", "coal_reserve_remaining"]}], "writes": [{"table": "flu_cases", "columns": ["leadershiptraining", "coal_reserve_remaining"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"precipitation_data\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "precipitation_data", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 147;\nEOF\n", "labels": {"reads": [{"table": "acceptance", "columns": ["attribute_id", "habitat_name"]}], "writes": [{"table": "ocean_acidification", "columns": ["attribute_id", "habitat_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO financial_capability_id SELECT accommodation_type, highscore, has_parabens FROM cargos WHERE accommodation_type > 458\"\n", "labels": {"reads": [{"table": "cargos", "columns": ["accommodation_type", "highscore", "has_parabens"]}], "writes": [{"table": "financial_capability_id", "columns": ["accommodation_type", "highscore", "has_parabens"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model watertreatmentplants depends on courses\ndbt build --select watertreatmentplants --vars 'source: courses'\n", "labels": {"reads": [{"table": "courses", "columns": null}], "writes": [{"table": "watertreatmentplants", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"chemical_production_5\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO research SELECT reservoir_name, materialtype FROM features WHERE reservoir_name > 1\"\n", "labels": {"reads": [{"table": "features", "columns": ["reservoir_name", "materialtype"]}], "writes": [{"table": "research", "columns": ["reservoir_name", "materialtype"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table strategies --columns organisation_type,observation_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "strategies", "columns": ["organisation_type", "observation_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 133;\nSQL\n", "labels": {"reads": [{"table": "ads.sessions_hourly", "columns": ["service_details", "nurse"]}, {"table": "worker_union", "columns": ["show_name", "debate_id", "num_of_stock", "dates_active"]}], "writes": [{"table": "solar_farms", "columns": ["show_name", "debate_id", "num_of_stock", "dates_active"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT served_subscribers, inventoryid FROM underwater_trenches LIMIT 418\")\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO gamegenres SELECT waste_amount, community_type, screen_mode, incidentdate FROM paris_real_estate WHERE waste_amount > 119\")\n", "labels": {"reads": [{"table": "underwater_trenches", "columns": ["served_subscribers", "inventoryid"]}, {"table": "paris_real_estate", "columns": ["waste_amount", "community_type", "screen_mode", "incidentdate"]}], "writes": [{"table": "gamegenres", "columns": ["waste_amount", "community_type", "screen_mode", "incidentdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO safetytests SELECT coupon_amount, threats FROM daily_transaction_volume WHERE coupon_amount > 256\"], check=True)\n", "labels": {"reads": [{"table": "daily_transaction_volume", "columns": ["coupon_amount", "threats"]}], "writes": [{"table": "safetytests", "columns": ["coupon_amount", "threats"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sustainabilityratings (policyid, launched_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sustainabilityratings", "columns": ["policyid", "launched_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"container_receipts\").where(\"dt = current_date()\").writeTo(\"fabrics\").append()\n", "labels": {"reads": [{"table": "container_receipts", "columns": null}], "writes": [{"table": "fabrics", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = load_table(ctx, \"dysprosiumproduction\")\ndump_to_store(df, \"wrestler\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": null}], "writes": [{"table": "wrestler", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"yoga\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "yoga", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO eco_hotels SELECT a.game, b.union_id FROM street_markets a JOIN head b ON a.media_type_id = b.media_type_id\"\n", "labels": {"reads": [{"table": "street_markets", "columns": null}, {"table": "head", "columns": null}], "writes": [{"table": "eco_hotels", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table road_construction --columns enroll_grade,volunteer_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "road_construction", "columns": ["enroll_grade", "volunteer_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO vulnerabilities SELECT * FROM legacy\nspark.sql(\"INSERT INTO hydro_power SELECT date_of_latest_revision, project_category, feb, employmentdate FROM cargo_data WHERE date_of_latest_revision > 183\")\n", "labels": {"reads": [{"table": "cargo_data", "columns": ["date_of_latest_revision", "project_category", "feb", "employmentdate"]}], "writes": [{"table": "hydro_power", "columns": ["date_of_latest_revision", "project_category", "feb", "employmentdate"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"microfinance_clients\")\nsrc.write.insertInto(\"player\", overwrite=True)\n", "labels": {"reads": [{"table": "microfinance_clients", "columns": null}], "writes": [{"table": "player", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"continent\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "continent", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 107;\nSQL\n", "labels": {"reads": [{"table": "disaster_response", "columns": ["home_team_three_point", "startup_id"]}, {"table": "circular_economy", "columns": ["crossing", "amount_used", "clublocation", "population"]}], "writes": [{"table": "vessel_safety", "columns": ["crossing", "amount_used", "clublocation", "population"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT year, club_name FROM restorative_justice_center LIMIT 213\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "restorative_justice_center", "columns": ["year", "club_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"assessment_notes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"licenses\")\n", "labels": {"reads": [{"table": "assessment_notes", "columns": null}], "writes": [{"table": "licenses", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table victims --columns settlement_amount,energy_production --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "victims", "columns": ["settlement_amount", "energy_production"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"biotech_startups\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "biotech_startups", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table staff_roles --columns bus_id,citizen_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "staff_roles", "columns": ["bus_id", "citizen_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO plants SELECT scientific_name, restypename, bedtype FROM city.community_policing WHERE scientific_name > 243\")\n", "labels": {"reads": [{"table": "city.community_policing", "columns": ["scientific_name", "restypename", "bedtype"]}], "writes": [{"table": "plants", "columns": ["scientific_name", "restypename", "bedtype"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi_device_log_daily\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vesselarrivals\", conn)\ndf.to_sql(\"sites_me\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": null}], "writes": [{"table": "sites_me", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO weapons (contract_end_date, coverage_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "weapons", "columns": ["contract_end_date", "coverage_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table engineer_skills --columns event_name,detection_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "engineer_skills", "columns": ["event_name", "detection_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO specieswatertemp SELECT lesson_status_code, savings, event_id FROM food_assistance WHERE lesson_status_code > 453\")\n", "labels": {"reads": [{"table": "food_assistance", "columns": ["lesson_status_code", "savings", "event_id"]}], "writes": [{"table": "specieswatertemp", "columns": ["lesson_status_code", "savings", "event_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO labor_hours (campaign_name, quantitysold) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "labor_hours", "columns": ["campaign_name", "quantitysold"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_shipments_df\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.sessions_full\")\n", "labels": {"reads": [{"table": "dws_shipments_df", "columns": null}], "writes": [{"table": "stg.sessions_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO field5 SELECT 1\"\nlogger.info(msg)\nimport logging\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tree_species\", conn)\ndf.to_sql(\"birds\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tree_species", "columns": null}], "writes": [{"table": "birds", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.supply_volume > 78).all()\n# src table: customer_master_index\nengine.execute(\"INSERT INTO advisor SELECT * FROM customer_master_index\")\n", "labels": {"reads": [{"table": "customer_master_index", "columns": null}], "writes": [{"table": "advisor", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT organization_details, acc_type FROM tb_reports LIMIT 490\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO community_policing_events SELECT dishid, area_sqkm, averagespeed FROM ai_safety_incidents WHERE dishid > 461\")\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["organization_details", "acc_type"]}, {"table": "ai_safety_incidents", "columns": ["dishid", "area_sqkm", "averagespeed"]}], "writes": [{"table": "community_policing_events", "columns": ["dishid", "area_sqkm", "averagespeed"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO artist SELECT * FROM legacy\nspark.sql(\"INSERT INTO conservation_programs SELECT garment_name, rating_id, policyname, claim_header_id FROM section WHERE garment_name > 149\")\n", "labels": {"reads": [{"table": "section", "columns": ["garment_name", "rating_id", "policyname", "claim_header_id"]}], "writes": [{"table": "conservation_programs", "columns": ["garment_name", "rating_id", "policyname", "claim_header_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table attendee_demographics --columns hourid,saledate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "attendee_demographics", "columns": ["hourid", "saledate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO company SELECT export_country, ship_name FROM energy_efficiency_projects WHERE export_country > 419\"\n", "labels": {"reads": [{"table": "energy_efficiency_projects", "columns": ["export_country", "ship_name"]}], "writes": [{"table": "company", "columns": ["export_country", "ship_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dw_events_di depends on militarybases\ndbt build --models dw_events_di --vars '{\"source_table\":\"militarybases\"}'\n", "labels": {"reads": [{"table": "militarybases", "columns": null}], "writes": [{"table": "dw_events_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table emergency_categories --target-dir /tmp/land\n", "labels": {"reads": [{"table": "emergency_categories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO indigenous_communities SELECT num_libraries, studentid, indigenous, facility_name FROM list WHERE num_libraries > 130\")\n", "labels": {"reads": [{"table": "list", "columns": ["num_libraries", "studentid", "indigenous", "facility_name"]}], "writes": [{"table": "indigenous_communities", "columns": ["num_libraries", "studentid", "indigenous", "facility_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO tunnels SELECT tech, product_stock_number, dish_id, loan_amount FROM customer_size_diversity WHERE tech > 163\"\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": ["tech", "product_stock_number", "dish_id", "loan_amount"]}], "writes": [{"table": "tunnels", "columns": ["tech", "product_stock_number", "dish_id", "loan_amount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_table(ctx, \"ref_product_categories\")\nupsert_to_target(df, \"gender\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ref_product_categories", "columns": null}], "writes": [{"table": "gender", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bioprocesses\").toPandas()\ndf[[\"energy_efficiency_kwh_m2_year\", \"last_updated\"]].to_sql(\"stg.refunds_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "bioprocesses", "columns": null}], "writes": [{"table": "stg.refunds_daily", "columns": ["energy_efficiency_kwh_m2_year", "last_updated"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table marketing_budgets --target-dir /tmp/land\n", "labels": {"reads": [{"table": "marketing_budgets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sales_region SELECT retailer, trip_start_time, grant_type FROM west_providers WHERE retailer > 251\"\n", "labels": {"reads": [{"table": "west_providers", "columns": ["retailer", "trip_start_time", "grant_type"]}], "writes": [{"table": "sales_region", "columns": ["retailer", "trip_start_time", "grant_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO hotel_chains SELECT a.workout_type, b.followers FROM complaints a JOIN waste_generation b ON a.app_id = b.app_id\"\n", "labels": {"reads": [{"table": "complaints", "columns": null}, {"table": "waste_generation", "columns": null}], "writes": [{"table": "hotel_chains", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO manufacturer SELECT patentexpirationdate, tree_type_id FROM equipment_sales WHERE patentexpirationdate > 376\"], check=True)\n", "labels": {"reads": [{"table": "equipment_sales", "columns": ["patentexpirationdate", "tree_type_id"]}], "writes": [{"table": "manufacturer", "columns": ["patentexpirationdate", "tree_type_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO shariah_financing SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO humanitarianmissions SELECT claimtype, zone_name, living_wage, established_date FROM gameattendance WHERE claimtype > 176\"\n", "labels": {"reads": [{"table": "gameattendance", "columns": ["claimtype", "zone_name", "living_wage", "established_date"]}], "writes": [{"table": "humanitarianmissions", "columns": ["claimtype", "zone_name", "living_wage", "established_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO artist_info SELECT volunteerhourid, restaurant_id, head, how_to_get_there FROM coowners WHERE volunteerhourid > 242\"], check=True)\n", "labels": {"reads": [{"table": "coowners", "columns": ["volunteerhourid", "restaurant_id", "head", "how_to_get_there"]}], "writes": [{"table": "artist_info", "columns": ["volunteerhourid", "restaurant_id", "head", "how_to_get_there"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO fleet_management SELECT skill_description, start_therapy, review_score, event_count FROM academic_publications WHERE skill_description > 376\")\n", "labels": {"reads": [{"table": "academic_publications", "columns": ["skill_description", "start_therapy", "review_score", "event_count"]}], "writes": [{"table": "fleet_management", "columns": ["skill_description", "start_therapy", "review_score", "event_count"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO food_assistance SELECT founding_year, currency_code, offer_id, period FROM crop_temperature WHERE founding_year > 466\"\n", "labels": {"reads": [{"table": "crop_temperature", "columns": ["founding_year", "currency_code", "offer_id", "period"]}], "writes": [{"table": "food_assistance", "columns": ["founding_year", "currency_code", "offer_id", "period"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT reason, state_code FROM digital_trends LIMIT 227\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "digital_trends", "columns": ["reason", "state_code"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 178;\nSQL\n", "labels": {"reads": [{"table": "marine_species", "columns": ["adoption_date", "billingamount"]}, {"table": "adaptation_projects", "columns": ["resource", "menuitem"]}], "writes": [{"table": "intelligence_agents", "columns": ["resource", "menuitem"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO artist_demographics SELECT trackid, host, mental_health_resource_access, donator_name FROM therapy WHERE trackid > 205\"\n", "labels": {"reads": [{"table": "therapy", "columns": ["trackid", "host", "mental_health_resource_access", "donator_name"]}], "writes": [{"table": "artist_demographics", "columns": ["trackid", "host", "mental_health_resource_access", "donator_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"skincare_sales\").toPandas()\ndf[[\"daily_consumption\", \"main_services\"]].to_sql(\"department\", engine, index=False)\n", "labels": {"reads": [{"table": "skincare_sales", "columns": null}], "writes": [{"table": "department", "columns": ["daily_consumption", "main_services"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 375;\nSQL\n", "labels": {"reads": [{"table": "well_production", "columns": ["effort_id", "lieutenant_governor"]}, {"table": "ads.orders_daily", "columns": ["platformname", "policy_number"]}], "writes": [{"table": "ads.ads_refunds_hourly", "columns": ["platformname", "policy_number"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO agriculturalinnovations SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 462;\nEOF\n", "labels": {"reads": [{"table": "wastewatertreatment", "columns": ["category_id", "ppos", "reported_date", "applicant"]}], "writes": [{"table": "dws.dws_events_hourly", "columns": ["category_id", "ppos", "reported_date", "applicant"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"organic_cosmetics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"viewership\")\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": null}], "writes": [{"table": "viewership", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ads.risk_score SELECT * FROM legacy\nspark.sql(\"INSERT INTO maintenance_requests SELECT exoplanet, retailer_name FROM art WHERE exoplanet > 4\")\n", "labels": {"reads": [{"table": "art", "columns": ["exoplanet", "retailer_name"]}], "writes": [{"table": "maintenance_requests", "columns": ["exoplanet", "retailer_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO bi_refunds_daily SELECT a.claim_outcome_code, b.policy_description FROM runs a JOIN convictions b ON a.investment_amount = b.investment_amount\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "runs", "columns": null}, {"table": "convictions", "columns": null}], "writes": [{"table": "bi_refunds_daily", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 196;\nEOF\n", "labels": {"reads": [{"table": "causes_insert_2", "columns": ["case_status", "productionid", "group_name"]}], "writes": [{"table": "station", "columns": ["case_status", "productionid", "group_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mart.mart_member_point_hourly\").where(\"dt = current_date()\").writeTo(\"salesperson\").append()\n", "labels": {"reads": [{"table": "mart.mart_member_point_hourly", "columns": null}], "writes": [{"table": "salesperson", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"exoplanet_discoveries\");\ndf.write().mode(\"overwrite\").saveAsTable(\"gamesessions\");\n", "labels": {"reads": [{"table": "exoplanet_discoveries", "columns": null}], "writes": [{"table": "gamesessions", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads.inventory_di (financial_capability_score, claim_header_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads.inventory_di", "columns": ["financial_capability_score", "claim_header_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO military_personnel SELECT parent_organization_id, state_province FROM research_staff WHERE parent_organization_id > 298\")\n", "labels": {"reads": [{"table": "research_staff", "columns": ["parent_organization_id", "state_province"]}], "writes": [{"table": "military_personnel", "columns": ["parent_organization_id", "state_province"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO catalog_contents SELECT claimdate, animal_name FROM monitoring_zones WHERE claimdate > 454\")\n", "labels": {"reads": [{"table": "monitoring_zones", "columns": ["claimdate", "animal_name"]}], "writes": [{"table": "catalog_contents", "columns": ["claimdate", "animal_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO heritage_sites_3 SELECT sustainabilityrating, oil_production_q4_2021, artwork_name, active FROM green_building_materials WHERE sustainabilityrating > 333\")\n", "labels": {"reads": [{"table": "green_building_materials", "columns": ["sustainabilityrating", "oil_production_q4_2021", "artwork_name", "active"]}], "writes": [{"table": "heritage_sites_3", "columns": ["sustainabilityrating", "oil_production_q4_2021", "artwork_name", "active"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO aid_missions SELECT profits_in_billion, garment_material, game_name FROM labor_productivity WHERE profits_in_billion > 333\"\n", "labels": {"reads": [{"table": "labor_productivity", "columns": ["profits_in_billion", "garment_material", "game_name"]}], "writes": [{"table": "aid_missions", "columns": ["profits_in_billion", "garment_material", "game_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO salesdata SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM list\", conn)\ndf.to_sql(\"mart.mart_member_point_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "list", "columns": null}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"trenches\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"reo_production\")\n", "labels": {"reads": [{"table": "trenches", "columns": null}], "writes": [{"table": "reo_production", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"affiliated_with\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "affiliated_with", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table carbon_offsets.carbon_offsets --columns generation_date,unit_price --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "carbon_offsets.carbon_offsets", "columns": ["generation_date", "unit_price"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT product_type_code, restaurant_name FROM volunteer_events LIMIT 77\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [{"table": "volunteer_events", "columns": ["product_type_code", "restaurant_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO job_postings SELECT center_id, nid, objectnumber, ironquantity FROM commercialbuildings WHERE center_id > 363\"\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": ["center_id", "nid", "objectnumber", "ironquantity"]}], "writes": [{"table": "job_postings", "columns": ["center_id", "nid", "objectnumber", "ironquantity"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO advisor SELECT * FROM legacy\ncur.execute(\"SELECT donorid, advisor FROM bi_campaigns_delta LIMIT 217\")\n", "labels": {"reads": [{"table": "bi_campaigns_delta", "columns": ["donorid", "advisor"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"biotech_startups\").where(\"dt = current_date()\").writeTo(\"apartments\").append()\n", "labels": {"reads": [{"table": "biotech_startups", "columns": null}], "writes": [{"table": "apartments", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table electric_vehicle_stats --target-dir /tmp/land\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 265;\nSQL\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": ["vehicle", "wildlife_type_id"]}, {"table": "global_tournament", "columns": ["ota_id", "bandmate", "startdate"]}], "writes": [{"table": "funding_rounds", "columns": ["ota_id", "bandmate", "startdate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO canada_cosmetics_preferences SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model timber_sales depends on incidents\ndbt run -s timber_sales --vars '{\"source_table\":\"incidents\"}'\n", "labels": {"reads": [{"table": "incidents", "columns": null}], "writes": [{"table": "timber_sales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"pediatricians\").where(\"dt = current_date()\").writeTo(\"lead_mines\").append()\n", "labels": {"reads": [{"table": "pediatricians", "columns": null}], "writes": [{"table": "lead_mines", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT park, event_location FROM healthcare_access_v2\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"player_stats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": ["park", "event_location"]}], "writes": [{"table": "player_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"school_bus\")\nsrc.write.insertInto(\"ref_product_categories\", overwrite=True)\n", "labels": {"reads": [{"table": "school_bus", "columns": null}], "writes": [{"table": "ref_product_categories", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vessel_positions SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO company_info SELECT energy_star_rating, dept_store_chain_id FROM equipment_maintenance WHERE energy_star_rating > 220\"\n", "labels": {"reads": [{"table": "equipment_maintenance", "columns": ["energy_star_rating", "dept_store_chain_id"]}], "writes": [{"table": "company_info", "columns": ["energy_star_rating", "dept_store_chain_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT appointment_date, disaster_id FROM hires LIMIT 225\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "hires", "columns": ["appointment_date", "disaster_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO maintenance_requests SELECT festival_id, artworkid, staff_address_id, ad_id FROM developers WHERE festival_id > 69\")\n", "labels": {"reads": [{"table": "developers", "columns": ["festival_id", "artworkid", "staff_address_id", "ad_id"]}], "writes": [{"table": "maintenance_requests", "columns": ["festival_id", "artworkid", "staff_address_id", "ad_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO user_workouts_march SELECT accreditation_type, clinic_id, product_description, shipmenttype FROM bi_refunds_daily WHERE accreditation_type > 345\"\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["accreditation_type", "clinic_id", "product_description", "shipmenttype"]}], "writes": [{"table": "user_workouts_march", "columns": ["accreditation_type", "clinic_id", "product_description", "shipmenttype"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.algorithmic_fairness_score > 157).all()\n# src table: station_emergencies\nengine.execute(\"INSERT INTO donationsbycause SELECT * FROM station_emergencies\")\n", "labels": {"reads": [{"table": "station_emergencies", "columns": null}], "writes": [{"table": "donationsbycause", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM marine_life_data\"\n", "labels": {"reads": [{"table": "marine_life_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table algorithmic_fairness --target-dir /tmp/land\n", "labels": {"reads": [{"table": "algorithmic_fairness", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"medical_facilities\");\ndf.write().mode(\"overwrite\").saveAsTable(\"arctic_weather\");\n", "labels": {"reads": [{"table": "medical_facilities", "columns": null}], "writes": [{"table": "arctic_weather", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO sales_2 SELECT ai_adoption_date, cause, city_code FROM ads_exposure_hourly WHERE ai_adoption_date > 437\");\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": ["ai_adoption_date", "cause", "city_code"]}], "writes": [{"table": "sales_2", "columns": ["ai_adoption_date", "cause", "city_code"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO daily_articles_by_category SELECT rating_id, market_details, annual_carbon_offsets, stuid FROM subjects WHERE rating_id > 76\")\n", "labels": {"reads": [{"table": "subjects", "columns": ["rating_id", "market_details", "annual_carbon_offsets", "stuid"]}], "writes": [{"table": "daily_articles_by_category", "columns": ["rating_id", "market_details", "annual_carbon_offsets", "stuid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table match --columns booking_id,dose --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "match", "columns": ["booking_id", "dose"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table factory_water --columns width,cuisine_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "factory_water", "columns": ["width", "cuisine_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO useracct SELECT individual_name, fund_name, customer_name FROM paintings WHERE individual_name > 127\")\n", "labels": {"reads": [{"table": "paintings", "columns": ["individual_name", "fund_name", "customer_name"]}], "writes": [{"table": "useracct", "columns": ["individual_name", "fund_name", "customer_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO spaceradar SELECT measurement_date, time_id, billing_city FROM travel_advisory WHERE measurement_date > 353\")\n", "labels": {"reads": [{"table": "travel_advisory", "columns": ["measurement_date", "time_id", "billing_city"]}], "writes": [{"table": "spaceradar", "columns": ["measurement_date", "time_id", "billing_city"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donor_state, host FROM nz_tourism LIMIT 234\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "nz_tourism", "columns": ["donor_state", "host"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO defenseprojects SELECT production_budget, exhibitions, delivery_status FROM environmental_impact_stats WHERE production_budget > 73\"\n", "labels": {"reads": [{"table": "environmental_impact_stats", "columns": ["production_budget", "exhibitions", "delivery_status"]}], "writes": [{"table": "defenseprojects", "columns": ["production_budget", "exhibitions", "delivery_status"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws.dws_events_hourly\")\nsrc.write.insertInto(\"wrestler\", overwrite=True)\n", "labels": {"reads": [{"table": "dws.dws_events_hourly", "columns": null}], "writes": [{"table": "wrestler", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO skills SELECT signup_date, transact_date FROM vulnerabilities WHERE signup_date > 327\"\n", "labels": {"reads": [{"table": "vulnerabilities", "columns": ["signup_date", "transact_date"]}], "writes": [{"table": "skills", "columns": ["signup_date", "transact_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO dws_products SELECT oil_volume, cropname FROM sustainable_warehouses WHERE oil_volume > 264\");\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": ["oil_volume", "cropname"]}], "writes": [{"table": "dws_products", "columns": ["oil_volume", "cropname"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO transportation_union SELECT menu_category, num_employees, transaction_type, teacher_id FROM mart.mart_users WHERE menu_category > 177\"\n", "labels": {"reads": [{"table": "mart.mart_users", "columns": ["menu_category", "num_employees", "transaction_type", "teacher_id"]}], "writes": [{"table": "transportation_union", "columns": ["menu_category", "num_employees", "transaction_type", "teacher_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ads_payments_hourly\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO esa_missions (connection, investmentdate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "esa_missions", "columns": ["connection", "investmentdate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 357;\nEOF\n", "labels": {"reads": [{"table": "country_landfill_capacity", "columns": ["timestamp", "investmenttype", "hoursspent"]}], "writes": [{"table": "satellitematerials", "columns": ["timestamp", "investmenttype", "hoursspent"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO community_health_centers SELECT a.workerid, b.complaint_status_code FROM athlete_wellbeing a JOIN vehicle_data b ON a.impact_id = b.impact_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": null}, {"table": "vehicle_data", "columns": null}], "writes": [{"table": "community_health_centers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO facility_production SELECT time_day, membership_id, council_id FROM ais WHERE time_day > 242\"\n", "labels": {"reads": [{"table": "ais", "columns": ["time_day", "membership_id", "council_id"]}], "writes": [{"table": "facility_production", "columns": ["time_day", "membership_id", "council_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO gamestats SELECT fleet_name, trade_name FROM minor_in WHERE fleet_name > 305\")\n", "labels": {"reads": [{"table": "minor_in", "columns": ["fleet_name", "trade_name"]}], "writes": [{"table": "gamestats", "columns": ["fleet_name", "trade_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO renewabletypes (creation_date, ocean) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "renewabletypes", "columns": ["creation_date", "ocean"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tracks SELECT citizens, maintenance_id FROM stock_levels WHERE citizens > 427\"], check=True)\n", "labels": {"reads": [{"table": "stock_levels", "columns": ["citizens", "maintenance_id"]}], "writes": [{"table": "tracks", "columns": ["citizens", "maintenance_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO organic_cosmetics SELECT date_of_transaction, chw_id, amount_piad, well_depth FROM labor_practices WHERE date_of_transaction > 248\"], check=True)\n", "labels": {"reads": [{"table": "labor_practices", "columns": ["date_of_transaction", "chw_id", "amount_piad", "well_depth"]}], "writes": [{"table": "organic_cosmetics", "columns": ["date_of_transaction", "chw_id", "amount_piad", "well_depth"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO premises SELECT virtual_tour_sessions, anomaly, production_quantity, cultural_significance FROM ads.ads_users_hourly WHERE virtual_tour_sessions > 276\"\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": ["virtual_tour_sessions", "anomaly", "production_quantity", "cultural_significance"]}], "writes": [{"table": "premises", "columns": ["virtual_tour_sessions", "anomaly", "production_quantity", "cultural_significance"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO food_justice_contributors SELECT don_name, end_speed, requestid, farm_name FROM highest_scores WHERE don_name > 231\");\n", "labels": {"reads": [{"table": "highest_scores", "columns": ["don_name", "end_speed", "requestid", "farm_name"]}], "writes": [{"table": "food_justice_contributors", "columns": ["don_name", "end_speed", "requestid", "farm_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO program_history SELECT a.agegroup, b.museumname FROM stg.coupon_use_delta a JOIN communitypolicing b ON a.investment_name = b.investment_name\"\n", "labels": {"reads": [{"table": "stg.coupon_use_delta", "columns": null}, {"table": "communitypolicing", "columns": null}], "writes": [{"table": "program_history", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO infrastructure_projects (length_meters, job_title_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "infrastructure_projects", "columns": ["length_meters", "job_title_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO musical SELECT artifactname, mgr_start_date FROM therapy WHERE artifactname > 494\");\n", "labels": {"reads": [{"table": "therapy", "columns": ["artifactname", "mgr_start_date"]}], "writes": [{"table": "musical", "columns": ["artifactname", "mgr_start_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO site SELECT * FROM legacy\ncur.execute(\"SELECT energy_generated, grant_type FROM electricvehicleadoption LIMIT 86\")\n", "labels": {"reads": [{"table": "electricvehicleadoption", "columns": ["energy_generated", "grant_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dw.shipments_df SELECT host_city, skill_description, tree_type_id FROM fan_purchases WHERE host_city > 470\"\n", "labels": {"reads": [{"table": "fan_purchases", "columns": ["host_city", "skill_description", "tree_type_id"]}], "writes": [{"table": "dw.shipments_df", "columns": ["host_city", "skill_description", "tree_type_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table mart.mart_shipments_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bridgerainfall\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"chemicalproducts\")\n", "labels": {"reads": [{"table": "bridgerainfall", "columns": null}], "writes": [{"table": "chemicalproducts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"organization_contact_individuals\")\npersist_to_sink(df, \"gender\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "organization_contact_individuals", "columns": null}], "writes": [{"table": "gender", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO al_jazeera_data SELECT sessiondate, condition_id, event_count FROM astronaut_missions WHERE sessiondate > 457\");\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": ["sessiondate", "condition_id", "event_count"]}], "writes": [{"table": "al_jazeera_data", "columns": ["sessiondate", "condition_id", "event_count"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"features\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"intelligence_personnel\")\n", "labels": {"reads": [{"table": "features", "columns": null}], "writes": [{"table": "intelligence_personnel", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stateinfrastructure SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.decision > 365).all()\n# src table: orders\nengine.execute(\"INSERT INTO fabric SELECT * FROM orders\")\n", "labels": {"reads": [{"table": "orders", "columns": null}], "writes": [{"table": "fabric", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO humanitarian_operations SELECT materialname, marketing_region_name FROM aquatic_species WHERE materialname > 434\"\n", "labels": {"reads": [{"table": "aquatic_species", "columns": ["materialname", "marketing_region_name"]}], "writes": [{"table": "humanitarian_operations", "columns": ["materialname", "marketing_region_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO rental SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ma_inspections SELECT productid, visit_details FROM dwd.products_hourly WHERE productid > 327\"], check=True)\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": ["productid", "visit_details"]}], "writes": [{"table": "ma_inspections", "columns": ["productid", "visit_details"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO defense_contractors SELECT shipmenttype, date_from, experience, stars FROM food_items WHERE shipmenttype > 257\"\n", "labels": {"reads": [{"table": "food_items", "columns": ["shipmenttype", "date_from", "experience", "stars"]}], "writes": [{"table": "defense_contractors", "columns": ["shipmenttype", "date_from", "experience", "stars"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO cities SELECT trip_city, vehicle_details, clinic_id FROM defenseprojects WHERE trip_city > 117\")\n", "labels": {"reads": [{"table": "defenseprojects", "columns": ["trip_city", "vehicle_details", "clinic_id"]}], "writes": [{"table": "cities", "columns": ["trip_city", "vehicle_details", "clinic_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO container_ships SELECT a.work_type, b.item_price FROM participation a JOIN maintenance_engineers b ON a.iata = b.iata\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "participation", "columns": null}, {"table": "maintenance_engineers", "columns": null}], "writes": [{"table": "container_ships", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM arcticwildlifereserve\", conn)\ndf.to_sql(\"florida_conservation_initiatives\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "arcticwildlifereserve", "columns": null}], "writes": [{"table": "florida_conservation_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO carbonoffsetinitiatives SELECT sale_year, opened_date, pages_per_minute_color FROM safety_research WHERE sale_year > 160\")\n", "labels": {"reads": [{"table": "safety_research", "columns": ["sale_year", "opened_date", "pages_per_minute_color"]}], "writes": [{"table": "carbonoffsetinitiatives", "columns": ["sale_year", "opened_date", "pages_per_minute_color"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO carbon_offsets SELECT fair_labor, attack_count FROM housing_investments WHERE fair_labor > 141\"\n", "labels": {"reads": [{"table": "housing_investments", "columns": ["fair_labor", "attack_count"]}], "writes": [{"table": "carbon_offsets", "columns": ["fair_labor", "attack_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ai_safety\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ai_safety", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gameattendance\").toPandas()\ndf[[\"cityname\", \"bias_score\"]].to_sql(\"autonomousvehicleaccidents\", engine, index=False)\n", "labels": {"reads": [{"table": "gameattendance", "columns": null}], "writes": [{"table": "autonomousvehicleaccidents", "columns": ["cityname", "bias_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM member_details\", conn)\ndf.to_sql(\"dysprosium_mines\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "member_details", "columns": null}], "writes": [{"table": "dysprosium_mines", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"product_details\");\ndf.write().mode(\"overwrite\").saveAsTable(\"electricvehicles\");\n", "labels": {"reads": [{"table": "product_details", "columns": null}], "writes": [{"table": "electricvehicles", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO overwatch_scores SELECT number_thousands, restaurantname FROM stg.campaigns_df WHERE number_thousands > 202\"\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": ["number_thousands", "restaurantname"]}], "writes": [{"table": "overwatch_scores", "columns": ["number_thousands", "restaurantname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO infrastructureprojects SELECT ll_activity, restypedescription FROM vehicle_safety_testing WHERE ll_activity > 186\"], check=True)\n", "labels": {"reads": [{"table": "vehicle_safety_testing", "columns": ["ll_activity", "restypedescription"]}], "writes": [{"table": "infrastructureprojects", "columns": ["ll_activity", "restypedescription"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO dwd.vendors SELECT do_value, policyname, ranking FROM military_sales WHERE do_value > 330\");\n", "labels": {"reads": [{"table": "military_sales", "columns": ["do_value", "policyname", "ranking"]}], "writes": [{"table": "dwd.vendors", "columns": ["do_value", "policyname", "ranking"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.meal_date > 245).all()\n# src table: crimes\nengine.execute(\"INSERT INTO marketing_budgets SELECT * FROM crimes\")\n", "labels": {"reads": [{"table": "crimes", "columns": null}], "writes": [{"table": "marketing_budgets", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT carbon_footprint, school_colors FROM financial_capability LIMIT 350\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "financial_capability", "columns": ["carbon_footprint", "school_colors"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO community_health_centers SELECT a.openingid, b.subscription_start_date FROM habitat a JOIN drills b ON a.itemid = b.itemid\"\n", "labels": {"reads": [{"table": "habitat", "columns": null}, {"table": "drills", "columns": null}], "writes": [{"table": "community_health_centers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table mart.mart_coupon_use_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"department_publications\").toPandas()\ndf[[\"store_id\", \"founding_year\"]].to_sql(\"maintenance_schedule\", engine, index=False)\n", "labels": {"reads": [{"table": "department_publications", "columns": null}], "writes": [{"table": "maintenance_schedule", "columns": ["store_id", "founding_year"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_campaigns_full SELECT * FROM legacy\ncur.execute(\"SELECT date_of_transaction, sportname FROM judges LIMIT 132\")\n", "labels": {"reads": [{"table": "judges", "columns": ["date_of_transaction", "sportname"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 60;\nSQL\n", "labels": {"reads": [{"table": "ocean_floor", "columns": ["drought_id", "emp_dob"]}, {"table": "phishing_targets", "columns": ["pricepergram", "product_subcategory", "physician", "habitat_name"]}], "writes": [{"table": "billstatus", "columns": ["pricepergram", "product_subcategory", "physician", "habitat_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.campaigns_full SELECT * FROM legacy\ncur.execute(\"SELECT budget, spacecraftid FROM artist_concerts LIMIT 47\")\n", "labels": {"reads": [{"table": "artist_concerts", "columns": ["budget", "spacecraftid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"organization\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"artistsales\")\n", "labels": {"reads": [{"table": "organization", "columns": null}], "writes": [{"table": "artistsales", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO travel_advisory SELECT satellite_name, caloric_content FROM teaches WHERE satellite_name > 300\"\n", "labels": {"reads": [{"table": "teaches", "columns": ["satellite_name", "caloric_content"]}], "writes": [{"table": "travel_advisory", "columns": ["satellite_name", "caloric_content"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO esportsevents (characteristic_name, production_usage) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "esportsevents", "columns": ["characteristic_name", "production_usage"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO sourcing SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 392;\nEOF\n", "labels": {"reads": [{"table": "agricultural_innovations", "columns": ["meal_id", "trench_id", "mean_sea_level_pressure_inches", "num_stops"]}], "writes": [{"table": "dorm", "columns": ["meal_id", "trench_id", "mean_sea_level_pressure_inches", "num_stops"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO healthcare_access_v2 SELECT creation, jun, cell_mobile_number, votes FROM workercontactinfo WHERE creation > 335\")\n", "labels": {"reads": [{"table": "workercontactinfo", "columns": ["creation", "jun", "cell_mobile_number", "votes"]}], "writes": [{"table": "healthcare_access_v2", "columns": ["creation", "jun", "cell_mobile_number", "votes"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO satellite_deployment SELECT revenueid, name_first, is_commercial FROM cerium_production WHERE revenueid > 191\");\n", "labels": {"reads": [{"table": "cerium_production", "columns": ["revenueid", "name_first", "is_commercial"]}], "writes": [{"table": "satellite_deployment", "columns": ["revenueid", "name_first", "is_commercial"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"stg.stg_campaigns_hourly\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"plants\")\n", "labels": {"reads": [{"table": "stg.stg_campaigns_hourly", "columns": null}], "writes": [{"table": "plants", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO timber_sales SELECT a.quantity, b.ll_hours FROM rent_arrears a JOIN civilcases b ON a.maintenanceid = b.maintenanceid\"\n", "labels": {"reads": [{"table": "rent_arrears", "columns": null}, {"table": "civilcases", "columns": null}], "writes": [{"table": "timber_sales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi_inventory_hourly --columns patient_age,party_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi_inventory_hourly", "columns": ["patient_age", "party_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dw.dw_member_point_di SELECT a.session_id, b.preference_rating FROM ota_revenue a JOIN appointments b ON a.customer_first_name = b.customer_first_name\"\n", "labels": {"reads": [{"table": "ota_revenue", "columns": null}, {"table": "appointments", "columns": null}], "writes": [{"table": "dw.dw_member_point_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"activity\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "activity", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"disaster_zones\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ads.ads_users_hourly\");\n", "labels": {"reads": [{"table": "disaster_zones", "columns": null}], "writes": [{"table": "ads.ads_users_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"tracks\")\npush_to_store(df, \"military_personnel_africa\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tracks", "columns": null}], "writes": [{"table": "military_personnel_africa", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table drama_workshop_groups --target-dir /tmp/land\n", "labels": {"reads": [{"table": "drama_workshop_groups", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO drivers SELECT accreditation_level, character, ai_id, grant_amount FROM mentalhealthproviders WHERE accreditation_level > 259\");\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": ["accreditation_level", "character", "ai_id", "grant_amount"]}], "writes": [{"table": "drivers", "columns": ["accreditation_level", "character", "ai_id", "grant_amount"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"performances\")\nwrite_to_output(df, \"sustainableproduction\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "performances", "columns": null}], "writes": [{"table": "sustainableproduction", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO urban_transportation SELECT * FROM legacy\ncur.execute(\"SELECT production_volume, cargoid FROM production_rare_earth_elements LIMIT 45\")\n", "labels": {"reads": [{"table": "production_rare_earth_elements", "columns": ["production_volume", "cargoid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ads.ads_exposure_daily SELECT claimtype, amount_piad, donationyear, personal_name FROM cargos WHERE claimtype > 99\"\n", "labels": {"reads": [{"table": "cargos", "columns": ["claimtype", "amount_piad", "donationyear", "personal_name"]}], "writes": [{"table": "ads.ads_exposure_daily", "columns": ["claimtype", "amount_piad", "donationyear", "personal_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.ads_exposure_di SELECT ll_id, garment_material, hourid FROM login_attempts WHERE ll_id > 413\"\n", "labels": {"reads": [{"table": "login_attempts", "columns": ["ll_id", "garment_material", "hourid"]}], "writes": [{"table": "ads.ads_exposure_di", "columns": ["ll_id", "garment_material", "hourid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO worker_scores SELECT fund_name, trip_date, line_1_number_building, num_of_component FROM bi.bi_sessions_daily WHERE fund_name > 10\"\n", "labels": {"reads": [{"table": "bi.bi_sessions_daily", "columns": ["fund_name", "trip_date", "line_1_number_building", "num_of_component"]}], "writes": [{"table": "worker_scores", "columns": ["fund_name", "trip_date", "line_1_number_building", "num_of_component"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dws.dws_campaigns_df SELECT exhibition_name, conferenceid FROM greenbuildings WHERE exhibition_name > 394\"\n", "labels": {"reads": [{"table": "greenbuildings", "columns": ["exhibition_name", "conferenceid"]}], "writes": [{"table": "dws.dws_campaigns_df", "columns": ["exhibition_name", "conferenceid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM co_ownership\"\n", "labels": {"reads": [{"table": "co_ownership", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO preferences SELECT registered_date, order_id, product_stock_number FROM trips WHERE registered_date > 278\")\n", "labels": {"reads": [{"table": "trips", "columns": ["registered_date", "order_id", "product_stock_number"]}], "writes": [{"table": "preferences", "columns": ["registered_date", "order_id", "product_stock_number"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.seal_species > 212).all()\n# src table: staff\nengine.execute(\"INSERT INTO healthcare_centers SELECT * FROM staff\")\n", "labels": {"reads": [{"table": "staff", "columns": null}], "writes": [{"table": "healthcare_centers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO fishcaught SELECT * FROM legacy\nspark.sql(\"INSERT INTO judges SELECT seating, drug FROM gamedesigndata WHERE seating > 85\")\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": ["seating", "drug"]}], "writes": [{"table": "judges", "columns": ["seating", "drug"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"concentrateprices\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "concentrateprices", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"dws.events\").where(\"dt = current_date()\").writeTo(\"cmi_cross_references\").append()\n", "labels": {"reads": [{"table": "dws.events", "columns": null}], "writes": [{"table": "cmi_cross_references", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO communitydevelopment SELECT driver_id, special_features, labor_id, functional_area_description FROM cosmetic_sales WHERE driver_id > 12\"\n", "labels": {"reads": [{"table": "cosmetic_sales", "columns": ["driver_id", "special_features", "labor_id", "functional_area_description"]}], "writes": [{"table": "communitydevelopment", "columns": ["driver_id", "special_features", "labor_id", "functional_area_description"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_safety_inspections\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"rating\")\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": null}], "writes": [{"table": "rating", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO transportation_per_country SELECT storename, spacecraft_model, causename, preference_rating FROM stg.stg_products_full WHERE storename > 355\"\n", "labels": {"reads": [{"table": "stg.stg_products_full", "columns": ["storename", "spacecraft_model", "causename", "preference_rating"]}], "writes": [{"table": "transportation_per_country", "columns": ["storename", "spacecraft_model", "causename", "preference_rating"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO haircaresales SELECT feature_details, engineer_id FROM has_allergy WHERE feature_details > 406\");\n", "labels": {"reads": [{"table": "has_allergy", "columns": ["feature_details", "engineer_id"]}], "writes": [{"table": "haircaresales", "columns": ["feature_details", "engineer_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO staff SELECT detection_date, creator, sales_channel, worker_name FROM financial_transactions WHERE detection_date > 7\"\n", "labels": {"reads": [{"table": "financial_transactions", "columns": ["detection_date", "creator", "sales_channel", "worker_name"]}], "writes": [{"table": "staff", "columns": ["detection_date", "creator", "sales_channel", "worker_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"assets\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"staff\")\n", "labels": {"reads": [{"table": "assets", "columns": null}], "writes": [{"table": "staff", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO song SELECT max_salary, revenue FROM volunteer_signups WHERE max_salary > 164\"\n", "labels": {"reads": [{"table": "volunteer_signups", "columns": ["max_salary", "revenue"]}], "writes": [{"table": "song", "columns": ["max_salary", "revenue"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO dws.dws_clicks_full SELECT election_cycle, art_type, enable_third_party_ads FROM transportation_union WHERE election_cycle > 323\"\n", "labels": {"reads": [{"table": "transportation_union", "columns": ["election_cycle", "art_type", "enable_third_party_ads"]}], "writes": [{"table": "dws.dws_clicks_full", "columns": ["election_cycle", "art_type", "enable_third_party_ads"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO bi.events_delta SELECT mascot, carbon_offset_tons, galleryname, crime_id FROM dws_events_df WHERE mascot > 271\")\n", "labels": {"reads": [{"table": "dws_events_df", "columns": ["mascot", "carbon_offset_tons", "galleryname", "crime_id"]}], "writes": [{"table": "bi.events_delta", "columns": ["mascot", "carbon_offset_tons", "galleryname", "crime_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.beds > 60).all()\n# src table: economic_diversification\nengine.execute(\"INSERT INTO loan SELECT * FROM economic_diversification\")\n", "labels": {"reads": [{"table": "economic_diversification", "columns": null}], "writes": [{"table": "loan", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table bridge --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bridge", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model course_attendance depends on all_documents\ndbt run --select course_attendance --vars '{\"src\":\"all_documents\"}'\n", "labels": {"reads": [{"table": "all_documents", "columns": null}], "writes": [{"table": "course_attendance", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO labour_productivity SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"safety_research\")\ndump_to_store(df, \"energy_storage\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "safety_research", "columns": null}], "writes": [{"table": "energy_storage", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_member_point_hourly\").toPandas()\ndf[[\"checkin\", \"injury_date\"]].to_sql(\"maintenance\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.mart_member_point_hourly", "columns": null}], "writes": [{"table": "maintenance", "columns": ["checkin", "injury_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bi.bi_campaigns_delta\");\ndf.write().mode(\"overwrite\").saveAsTable(\"factories_africa\");\n", "labels": {"reads": [{"table": "bi.bi_campaigns_delta", "columns": null}], "writes": [{"table": "factories_africa", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"sites_me\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "sites_me", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO publication SELECT to_address, creationyear, name_last FROM dw.exposure_di WHERE to_address > 430\"\n", "labels": {"reads": [{"table": "dw.exposure_di", "columns": ["to_address", "creationyear", "name_last"]}], "writes": [{"table": "publication", "columns": ["to_address", "creationyear", "name_last"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT invoice_details, business_size FROM military_personnel LIMIT 353\")\nrows = cur.fetchall()\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "military_personnel", "columns": ["invoice_details", "business_size"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO dwd_sessions_df SELECT party, hiredate FROM landfillcapacitybycountry WHERE party > 101\");\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": ["party", "hiredate"]}], "writes": [{"table": "dwd_sessions_df", "columns": ["party", "hiredate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.device_log_hourly SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bias_categories\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "bias_categories", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table state_water_usage --target-dir /tmp/land\n", "labels": {"reads": [{"table": "state_water_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO local_impact_japan (response_received_date, start_station_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "local_impact_japan", "columns": ["response_received_date", "start_station_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO broadband_plans SELECT hours_spent, drug, threats, spacecraft FROM dw.dw_risk_score_full WHERE hours_spent > 321\"\n", "labels": {"reads": [{"table": "dw.dw_risk_score_full", "columns": ["hours_spent", "drug", "threats", "spacecraft"]}], "writes": [{"table": "broadband_plans", "columns": ["hours_spent", "drug", "threats", "spacecraft"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"org_volunteer\").toPandas()\ndf[[\"killed\", \"isfirstattendee\"]].to_sql(\"innovation_trends\", engine, index=False)\n", "labels": {"reads": [{"table": "org_volunteer", "columns": null}], "writes": [{"table": "innovation_trends", "columns": ["killed", "isfirstattendee"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bridge\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bridge", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cerium_production\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"southchinasea.wells\")\n", "labels": {"reads": [{"table": "cerium_production", "columns": null}], "writes": [{"table": "southchinasea.wells", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO militaryoperations (altitude, bus_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "militaryoperations", "columns": ["altitude", "bus_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ods_vendors_daily --columns fleet_id,num_owners --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ods_vendors_daily", "columns": ["fleet_id", "num_owners"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT host_city_id, threats FROM electric_vehicle_stats LIMIT 245\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO courts SELECT materialname, strainid, restaurant, serving_size FROM mars_missions WHERE materialname > 306\")\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": ["host_city_id", "threats"]}, {"table": "mars_missions", "columns": ["materialname", "strainid", "restaurant", "serving_size"]}], "writes": [{"table": "courts", "columns": ["materialname", "strainid", "restaurant", "serving_size"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO state_budget SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"medals\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"prepaid_mobile\")\n", "labels": {"reads": [{"table": "medals", "columns": null}], "writes": [{"table": "prepaid_mobile", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"environmental_impact\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"host\")\n", "labels": {"reads": [{"table": "environmental_impact", "columns": null}], "writes": [{"table": "host", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO us_cities SELECT email, organic_matter FROM player_sessions WHERE email > 224\"\n", "labels": {"reads": [{"table": "player_sessions", "columns": ["email", "organic_matter"]}], "writes": [{"table": "us_cities", "columns": ["email", "organic_matter"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vrgames\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"company_info\")\n", "labels": {"reads": [{"table": "vrgames", "columns": null}], "writes": [{"table": "company_info", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM browser\"\n", "labels": {"reads": [{"table": "browser", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO bi.products_daily SELECT rural_area, birth_place, transactions, store_id FROM tech_workers_union WHERE rural_area > 24\")\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": ["rural_area", "birth_place", "transactions", "store_id"]}], "writes": [{"table": "bi.products_daily", "columns": ["rural_area", "birth_place", "transactions", "store_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 499;\nEOF\n", "labels": {"reads": [{"table": "government_transparency", "columns": ["away_team_score", "incidenttype", "composer"]}], "writes": [{"table": "neighborhoods", "columns": ["away_team_score", "incidenttype", "composer"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 420;\nEOF\n", "labels": {"reads": [{"table": "legal_technology_funding", "columns": ["professionalid", "points"]}], "writes": [{"table": "budget_allocations", "columns": ["professionalid", "points"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO results SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO totalenergyproduction SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO caseattorneys SELECT contact_number, exhibitionname, bill_id, policytype FROM ads.ads_events_df WHERE contact_number > 42\");\n", "labels": {"reads": [{"table": "ads.ads_events_df", "columns": ["contact_number", "exhibitionname", "bill_id", "policytype"]}], "writes": [{"table": "caseattorneys", "columns": ["contact_number", "exhibitionname", "bill_id", "policytype"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"driverstandings\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "driverstandings", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table smart_contracts_transactions --target-dir /tmp/land\n", "labels": {"reads": [{"table": "smart_contracts_transactions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM shops\"\n", "labels": {"reads": [{"table": "shops", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO researchers SELECT dob, energy_source, restaurant FROM military_personnel_africa WHERE dob > 59\")\n", "labels": {"reads": [{"table": "military_personnel_africa", "columns": ["dob", "energy_source", "restaurant"]}], "writes": [{"table": "researchers", "columns": ["dob", "energy_source", "restaurant"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT mgr_start_date, vegetable FROM swimmer LIMIT 50\")\nif not rows:\n logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO stg.stg_products_delta SELECT scores, headquarter FROM ref_calendar WHERE scores > 169\")\n", "labels": {"reads": [{"table": "swimmer", "columns": ["mgr_start_date", "vegetable"]}, {"table": "ref_calendar", "columns": ["scores", "headquarter"]}], "writes": [{"table": "stg.stg_products_delta", "columns": ["scores", "headquarter"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO public.ev_sales SELECT medicine_id, founded_year FROM cargo_handling WHERE medicine_id > 62\"\n", "labels": {"reads": [{"table": "cargo_handling", "columns": ["medicine_id", "founded_year"]}], "writes": [{"table": "public.ev_sales", "columns": ["medicine_id", "founded_year"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"excavations\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "excavations", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"recall_reports\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"assignedto\")\n", "labels": {"reads": [{"table": "recall_reports", "columns": null}], "writes": [{"table": "assignedto", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"browser\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "browser", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO indigenous_food_systems SELECT health_equity_metric_3, country_of_origin, disaster_id FROM traffic_violations WHERE health_equity_metric_3 > 340\"\n", "labels": {"reads": [{"table": "traffic_violations", "columns": ["health_equity_metric_3", "country_of_origin", "disaster_id"]}], "writes": [{"table": "indigenous_food_systems", "columns": ["health_equity_metric_3", "country_of_origin", "disaster_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table restorative_justice_center --target-dir /tmp/land\n", "labels": {"reads": [{"table": "restorative_justice_center", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 132;\nEOF\n", "labels": {"reads": [{"table": "recyclingrates", "columns": ["spacecraft_model", "ethical_manufacturing"]}], "writes": [{"table": "bi.bi_inventory_di", "columns": ["spacecraft_model", "ethical_manufacturing"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"weekly_weather\");\ndf.write().mode(\"overwrite\").saveAsTable(\"user_video_view\");\n", "labels": {"reads": [{"table": "weekly_weather", "columns": null}], "writes": [{"table": "user_video_view", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO category_revenue SELECT * FROM legacy\nspark.sql(\"INSERT INTO bi.bi_risk_score_full SELECT monthly_rental, days_held FROM satellite_deployment WHERE monthly_rental > 33\")\n", "labels": {"reads": [{"table": "satellite_deployment", "columns": ["monthly_rental", "days_held"]}], "writes": [{"table": "bi.bi_risk_score_full", "columns": ["monthly_rental", "days_held"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO total_capacity SELECT art, acidity FROM climate_communication WHERE art > 458\"\n", "labels": {"reads": [{"table": "climate_communication", "columns": ["art", "acidity"]}], "writes": [{"table": "total_capacity", "columns": ["art", "acidity"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO recovery_program SELECT competition, postalcode, monthlyactiveusers, dependent_name FROM volunteer_registration WHERE competition > 7\"\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": ["competition", "postalcode", "monthlyactiveusers", "dependent_name"]}], "writes": [{"table": "recovery_program", "columns": ["competition", "postalcode", "monthlyactiveusers", "dependent_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO station_company SELECT * FROM legacy\nspark.sql(\"INSERT INTO region SELECT degrees, sensor_reading, category_id, annual_revenue FROM sponsor_trials WHERE degrees > 395\")\n", "labels": {"reads": [{"table": "sponsor_trials", "columns": ["degrees", "sensor_reading", "category_id", "annual_revenue"]}], "writes": [{"table": "region", "columns": ["degrees", "sensor_reading", "category_id", "annual_revenue"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi.bi_orders_delta (min_depth, museum_details) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_orders_delta", "columns": ["min_depth", "museum_details"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dali SELECT yield_id, district_id, date_of_latest_revision, volunteerjoindate FROM watertreatmentplants WHERE yield_id > 323\"\n", "labels": {"reads": [{"table": "watertreatmentplants", "columns": ["yield_id", "district_id", "date_of_latest_revision", "volunteerjoindate"]}], "writes": [{"table": "dali", "columns": ["yield_id", "district_id", "date_of_latest_revision", "volunteerjoindate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wastewatertreatment SELECT * FROM legacy\ncur.execute(\"SELECT partner, email_address FROM donorgender LIMIT 484\")\n", "labels": {"reads": [{"table": "donorgender", "columns": ["partner", "email_address"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"experience\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"platformg\")\n", "labels": {"reads": [{"table": "experience", "columns": null}], "writes": [{"table": "platformg", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"vr_adopters\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "vr_adopters", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO militarydrones SELECT crop_name, instrument, publication_id FROM stores_2 WHERE crop_name > 373\")\n", "labels": {"reads": [{"table": "stores_2", "columns": ["crop_name", "instrument", "publication_id"]}], "writes": [{"table": "militarydrones", "columns": ["crop_name", "instrument", "publication_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"county\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"crops\")\n", "labels": {"reads": [{"table": "county", "columns": null}], "writes": [{"table": "crops", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO stock_levels SELECT store_name, sqft, storename FROM dishes WHERE store_name > 262\")\n", "labels": {"reads": [{"table": "dishes", "columns": ["store_name", "sqft", "storename"]}], "writes": [{"table": "stock_levels", "columns": ["store_name", "sqft", "storename"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO dwd.dwd_orders_di SELECT rebounds, clubname FROM satellites_by_country WHERE rebounds > 402\"\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": ["rebounds", "clubname"]}], "writes": [{"table": "dwd.dwd_orders_di", "columns": ["rebounds", "clubname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"humanitarian_aid\");\ndf.write().mode(\"overwrite\").saveAsTable(\"artists_valuation\");\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": null}], "writes": [{"table": "artists_valuation", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO park SELECT taxi_model, workshop_name FROM donationhistory WHERE taxi_model > 260\")\n", "labels": {"reads": [{"table": "donationhistory", "columns": ["taxi_model", "workshop_name"]}], "writes": [{"table": "park", "columns": ["taxi_model", "workshop_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT state_id, visitid FROM dws_events_df LIMIT 153\")\nimport logging\nspark.sql(\"INSERT INTO military_aircraft_maintenance SELECT issues, completion_year FROM budget WHERE issues > 421\")\n", "labels": {"reads": [{"table": "dws_events_df", "columns": ["state_id", "visitid"]}, {"table": "budget", "columns": ["issues", "completion_year"]}], "writes": [{"table": "military_aircraft_maintenance", "columns": ["issues", "completion_year"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO na_schema.hospitals (eia_date, attack_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "na_schema.hospitals", "columns": ["eia_date", "attack_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO carbon_sequestration SELECT * FROM legacy\nspark.sql(\"INSERT INTO sales SELECT plantlocation, cityid, chip_model, acidity_level FROM ticket_sales WHERE plantlocation > 64\")\n", "labels": {"reads": [{"table": "ticket_sales", "columns": ["plantlocation", "cityid", "chip_model", "acidity_level"]}], "writes": [{"table": "sales", "columns": ["plantlocation", "cityid", "chip_model", "acidity_level"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO diversity SELECT count_date, shipmenttype FROM virtual_tour_revenue WHERE count_date > 103\"\n", "labels": {"reads": [{"table": "virtual_tour_revenue", "columns": ["count_date", "shipmenttype"]}], "writes": [{"table": "diversity", "columns": ["count_date", "shipmenttype"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO workplace_safety SELECT image_data, mappingid, investmenttype, playtime FROM safe_dataset WHERE image_data > 169\"\n", "labels": {"reads": [{"table": "safe_dataset", "columns": ["image_data", "mappingid", "investmenttype", "playtime"]}], "writes": [{"table": "workplace_safety", "columns": ["image_data", "mappingid", "investmenttype", "playtime"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"water_sources\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"restorative_justice_sentences\")\n", "labels": {"reads": [{"table": "water_sources", "columns": null}], "writes": [{"table": "restorative_justice_sentences", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ai_safety\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"carbon_offsets.carbon_offsets\")\n", "labels": {"reads": [{"table": "ai_safety", "columns": null}], "writes": [{"table": "carbon_offsets.carbon_offsets", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model member_point depends on food_justice_contributors\ndbt build -s member_point --vars 'source: food_justice_contributors'\n", "labels": {"reads": [{"table": "food_justice_contributors", "columns": null}], "writes": [{"table": "member_point", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_safety_inspections\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hoursspent > 30).all()\n# src table: na_schema.hospitals\nengine.execute(\"INSERT INTO spacecraft SELECT * FROM na_schema.hospitals\")\n", "labels": {"reads": [{"table": "na_schema.hospitals", "columns": null}], "writes": [{"table": "spacecraft", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"local_impact\")\npush_to_warehouse(df, \"stations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "local_impact", "columns": null}], "writes": [{"table": "stations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO policy_advocacy SELECT a.grant_type, b.worker_id FROM status a JOIN educationprograms b ON a.mgr_start_date = b.mgr_start_date\"\n", "labels": {"reads": [{"table": "status", "columns": null}, {"table": "educationprograms", "columns": null}], "writes": [{"table": "policy_advocacy", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"safety_violations\").toPandas()\ndf[[\"customername\", \"studentid\"]].to_sql(\"militaryequipmentsales\", engine, index=False)\n", "labels": {"reads": [{"table": "safety_violations", "columns": null}], "writes": [{"table": "militaryequipmentsales", "columns": ["customername", "studentid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cloud_cover, workshop_id FROM mart.mart_campaigns_daily LIMIT 155\")\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO drama_workshop_groups SELECT financially_capable, sport, awardid FROM communityengagements WHERE financially_capable > 237\")\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": ["cloud_cover", "workshop_id"]}, {"table": "communityengagements", "columns": ["financially_capable", "sport", "awardid"]}], "writes": [{"table": "drama_workshop_groups", "columns": ["financially_capable", "sport", "awardid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"problem_log\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"user_video_view\")\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": [{"table": "user_video_view", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 92;\nEOF\n", "labels": {"reads": [{"table": "art_workshops", "columns": ["production", "job_category", "number_city_affected", "volunteer_hours"]}], "writes": [{"table": "emergencies", "columns": ["production", "job_category", "number_city_affected", "volunteer_hours"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 109;\nSQL\n", "labels": {"reads": [{"table": "electronics_factories", "columns": ["job_title", "attorney_id"]}, {"table": "bi.bi_inventory_full", "columns": ["campusfee", "founder"]}], "writes": [{"table": "student", "columns": ["campusfee", "founder"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hall_of_fame\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hall_of_fame", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"imagery_archive\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"residents_services\")\n", "labels": {"reads": [{"table": "imagery_archive", "columns": null}], "writes": [{"table": "residents_services", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO athlete_wellbeing SELECT amount_used, manufacturerid, storm_id, horizontal_bar_points FROM bi.clicks_df WHERE amount_used > 31\"\n", "labels": {"reads": [{"table": "bi.clicks_df", "columns": ["amount_used", "manufacturerid", "storm_id", "horizontal_bar_points"]}], "writes": [{"table": "athlete_wellbeing", "columns": ["amount_used", "manufacturerid", "storm_id", "horizontal_bar_points"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO contracts SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_source(ctx, \"mart.mart_users_df\")\nsink_to_store(df, \"healthcare\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_users_df", "columns": null}], "writes": [{"table": "healthcare", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO ads.orders_daily SELECT frequency, stageposition, license_plate FROM architect WHERE frequency > 345\")\n", "labels": {"reads": [{"table": "architect", "columns": ["frequency", "stageposition", "license_plate"]}], "writes": [{"table": "ads.orders_daily", "columns": ["frequency", "stageposition", "license_plate"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"sustainable_practices\").where(\"dt = current_date()\").writeTo(\"broadband_customers_global\").append()\n", "labels": {"reads": [{"table": "sustainable_practices", "columns": null}], "writes": [{"table": "broadband_customers_global", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cases SELECT is_autonomous, restorative_justice, certification_id FROM train_station WHERE is_autonomous > 343\"], check=True)\n", "labels": {"reads": [{"table": "train_station", "columns": ["is_autonomous", "restorative_justice", "certification_id"]}], "writes": [{"table": "cases", "columns": ["is_autonomous", "restorative_justice", "certification_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"vehicle_prices\");\ndf.write().mode(\"overwrite\").saveAsTable(\"landfillcapacitybycountry\");\n", "labels": {"reads": [{"table": "vehicle_prices", "columns": null}], "writes": [{"table": "landfillcapacitybycountry", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 491;\nSQL\n", "labels": {"reads": [{"table": "equipment", "columns": ["completion_year", "attendees"]}, {"table": "climate_communication_projects", "columns": ["program_type", "total_attendance", "accommodation_type"]}], "writes": [{"table": "social_good_education", "columns": ["program_type", "total_attendance", "accommodation_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table safe_dataset --target-dir /tmp/land\n", "labels": {"reads": [{"table": "safe_dataset", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO gamereviews SELECT max_depth, is_valid FROM outcomes WHERE max_depth > 343\"], check=True)\n", "labels": {"reads": [{"table": "outcomes", "columns": ["max_depth", "is_valid"]}], "writes": [{"table": "gamereviews", "columns": ["max_depth", "is_valid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi.bi_payments_full SELECT * FROM legacy\ncur.execute(\"SELECT gendername, dispensary_name FROM neighborhoods LIMIT 466\")\n", "labels": {"reads": [{"table": "neighborhoods", "columns": ["gendername", "dispensary_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"fairtradecertifications\").toPandas()\ndf[[\"fundingamount\", \"province_id\"]].to_sql(\"basketball_teams\", engine, index=False)\n", "labels": {"reads": [{"table": "fairtradecertifications", "columns": null}], "writes": [{"table": "basketball_teams", "columns": ["fundingamount", "province_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"energy_consumption\").where(\"dt = current_date()\").writeTo(\"wrestler\").append()\n", "labels": {"reads": [{"table": "energy_consumption", "columns": null}], "writes": [{"table": "wrestler", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO temperature_data SELECT missionid, capacity_mw, preference_rating, entryid FROM stg_users_daily WHERE missionid > 353\"\n", "labels": {"reads": [{"table": "stg_users_daily", "columns": ["missionid", "capacity_mw", "preference_rating", "entryid"]}], "writes": [{"table": "temperature_data", "columns": ["missionid", "capacity_mw", "preference_rating", "entryid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dwd.dwd_inventory_hourly\")\nsrc.write.insertInto(\"provinces\", overwrite=True)\n", "labels": {"reads": [{"table": "dwd.dwd_inventory_hourly", "columns": null}], "writes": [{"table": "provinces", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO digital_trends SELECT sale_id, running_time, observation_id FROM accessible_tech_categories WHERE sale_id > 496\")\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": ["sale_id", "running_time", "observation_id"]}], "writes": [{"table": "digital_trends", "columns": ["sale_id", "running_time", "observation_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO minor_in SELECT trainingid, language FROM waterconservationbudget WHERE trainingid > 24\")\n", "labels": {"reads": [{"table": "waterconservationbudget", "columns": ["trainingid", "language"]}], "writes": [{"table": "minor_in", "columns": ["trainingid", "language"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.state_code > 209).all()\n# src table: wind_energy_projects\nengine.execute(\"INSERT INTO circular_supply_chain_products SELECT * FROM wind_energy_projects\")\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": null}], "writes": [{"table": "circular_supply_chain_products", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.program_date > 329).all()\n# src table: loan\nengine.execute(\"INSERT INTO fieldd_info SELECT * FROM loan\")\n", "labels": {"reads": [{"table": "loan", "columns": null}], "writes": [{"table": "fieldd_info", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO billstatus SELECT closuredate, consumer_id FROM ods.products_hourly WHERE closuredate > 8\")\n", "labels": {"reads": [{"table": "ods.products_hourly", "columns": ["closuredate", "consumer_id"]}], "writes": [{"table": "billstatus", "columns": ["closuredate", "consumer_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO docking (goldquantity, cinema_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "docking", "columns": ["goldquantity", "cinema_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table farmers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "farmers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table textile_waste --columns active_from_date,review_rating --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "textile_waste", "columns": ["active_from_date", "review_rating"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO high_risk SELECT transaction_date, model FROM sustainabilityratings WHERE transaction_date > 231\")\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": ["transaction_date", "model"]}], "writes": [{"table": "high_risk", "columns": ["transaction_date", "model"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO sustainableproduction SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT daily_sales, product_type_code FROM genderdistribution\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"participation\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "genderdistribution", "columns": ["daily_sales", "product_type_code"]}], "writes": [{"table": "participation", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"tourdifferences\")\nsrc.write.insertInto(\"individual\", overwrite=True)\n", "labels": {"reads": [{"table": "tourdifferences", "columns": null}], "writes": [{"table": "individual", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO co2_emission_reduction SELECT attribute_id, order_date FROM regular_order_products WHERE attribute_id > 103\"\n", "labels": {"reads": [{"table": "regular_order_products", "columns": ["attribute_id", "order_date"]}], "writes": [{"table": "co2_emission_reduction", "columns": ["attribute_id", "order_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO fruitimport SELECT lastname, other_hotel_details, strategy_id FROM beauty_products WHERE lastname > 182\");\n", "labels": {"reads": [{"table": "beauty_products", "columns": ["lastname", "other_hotel_details", "strategy_id"]}], "writes": [{"table": "fruitimport", "columns": ["lastname", "other_hotel_details", "strategy_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO social_impact_bonds SELECT funder, attendeeid, activity, incidentid FROM bridges WHERE funder > 240\")\n", "labels": {"reads": [{"table": "bridges", "columns": ["funder", "attendeeid", "activity", "incidentid"]}], "writes": [{"table": "social_impact_bonds", "columns": ["funder", "attendeeid", "activity", "incidentid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vehicle_safety_testing (court_id, rate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vehicle_safety_testing", "columns": ["court_id", "rate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT problem_log_id, strat_name FROM australian_states LIMIT 30\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "australian_states", "columns": ["problem_log_id", "strat_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 311;\nEOF\n", "labels": {"reads": [{"table": "savings", "columns": ["num_cases", "partid", "passengers", "issue_month"]}], "writes": [{"table": "vocals", "columns": ["num_cases", "partid", "passengers", "issue_month"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO climate_communication (donorage, doctorsper1000) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "climate_communication", "columns": ["donorage", "doctorsper1000"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO revenue SELECT screening_id, material_id FROM inmates WHERE screening_id > 211\")\n", "labels": {"reads": [{"table": "inmates", "columns": ["screening_id", "material_id"]}], "writes": [{"table": "revenue", "columns": ["screening_id", "material_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"carbon_prices\");\ndf.write().mode(\"overwrite\").saveAsTable(\"genetics.experiments\");\n", "labels": {"reads": [{"table": "carbon_prices", "columns": null}], "writes": [{"table": "genetics.experiments", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO heritagesites SELECT marketing_region_name, safety_score, contributiondate FROM visits WHERE marketing_region_name > 57\");\n", "labels": {"reads": [{"table": "visits", "columns": ["marketing_region_name", "safety_score", "contributiondate"]}], "writes": [{"table": "heritagesites", "columns": ["marketing_region_name", "safety_score", "contributiondate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.campaigns_di SELECT cargo, curator, dname FROM excavations WHERE cargo > 73\"\n", "labels": {"reads": [{"table": "excavations", "columns": ["cargo", "curator", "dname"]}], "writes": [{"table": "ads.campaigns_di", "columns": ["cargo", "curator", "dname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vaccinations (incident, added_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vaccinations", "columns": ["incident", "added_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO species_data SELECT * FROM legacy\ncur.execute(\"SELECT log_entry_description, accommodationtype FROM species_data LIMIT 86\")\n", "labels": {"reads": [{"table": "species_data", "columns": ["log_entry_description", "accommodationtype"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT financial_capability_score, revenueid FROM stg.stg_users_full LIMIT 110\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "stg.stg_users_full", "columns": ["financial_capability_score", "revenueid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads.ads_shipments_delta\"\n", "labels": {"reads": [{"table": "ads.ads_shipments_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dali SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO reo_production SELECT union_name, station_name, end_station_name, sensor_type FROM investments_esg WHERE union_name > 340\")\n", "labels": {"reads": [{"table": "investments_esg", "columns": ["union_name", "station_name", "end_station_name", "sensor_type"]}], "writes": [{"table": "reo_production", "columns": ["union_name", "station_name", "end_station_name", "sensor_type"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 237;\nEOF\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["electoral_register_id", "trips", "hotel_id"]}], "writes": [{"table": "yttrium_production", "columns": ["electoral_register_id", "trips", "hotel_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ca_menu_items --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO clinical_trials SELECT community_id, attorney FROM stay WHERE community_id > 482\")\n", "labels": {"reads": [{"table": "stay", "columns": ["community_id", "attorney"]}], "writes": [{"table": "clinical_trials", "columns": ["community_id", "attorney"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"school_roster\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"inspection\")\n", "labels": {"reads": [{"table": "school_roster", "columns": null}], "writes": [{"table": "inspection", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mining_companies SELECT faculty, endtime, cargo_id FROM pets WHERE faculty > 118\"\n", "labels": {"reads": [{"table": "pets", "columns": ["faculty", "endtime", "cargo_id"]}], "writes": [{"table": "mining_companies", "columns": ["faculty", "endtime", "cargo_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO collective_bargaining SELECT precip, individual_middle_name, vaccination_status FROM mobile_plans WHERE precip > 280\"], check=True)\n", "labels": {"reads": [{"table": "mobile_plans", "columns": ["precip", "individual_middle_name", "vaccination_status"]}], "writes": [{"table": "collective_bargaining", "columns": ["precip", "individual_middle_name", "vaccination_status"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO mart_cart_item_di SELECT savingsid, fleet_series, transaction_product, date_left_staff FROM provider_training WHERE savingsid > 178\")\n", "labels": {"reads": [{"table": "provider_training", "columns": ["savingsid", "fleet_series", "transaction_product", "date_left_staff"]}], "writes": [{"table": "mart_cart_item_di", "columns": ["savingsid", "fleet_series", "transaction_product", "date_left_staff"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model diversification_projects depends on agroecology_practices\ndbt build --models diversification_projects --vars '{\"source_table\":\"agroecology_practices\"}'\n", "labels": {"reads": [{"table": "agroecology_practices", "columns": null}], "writes": [{"table": "diversification_projects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table inclusive_housing --target-dir /tmp/land\n", "labels": {"reads": [{"table": "inclusive_housing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO marine_mammals SELECT a.spent, b.detection_date FROM stg.stg_clicks_delta a JOIN fault_log_parts b ON a.allocation_date = b.allocation_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.stg_clicks_delta", "columns": null}, {"table": "fault_log_parts", "columns": null}], "writes": [{"table": "marine_mammals", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_users_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "stg.stg_users_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model india_ingredient_sourcing depends on wellbeing_programs\ndbt build --models india_ingredient_sourcing --vars '{\"source_table\":\"wellbeing_programs\"}'\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": null}], "writes": [{"table": "india_ingredient_sourcing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT train_type, stockid FROM document_structures\", engine)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nimport logging\ndf.to_sql(\"taxi_data\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "document_structures", "columns": ["train_type", "stockid"]}], "writes": [{"table": "taxi_data", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"pipelines\")\nsrc.write.insertInto(\"acidification_data\", overwrite=True)\n", "labels": {"reads": [{"table": "pipelines", "columns": null}], "writes": [{"table": "acidification_data", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 6;\nEOF\n", "labels": {"reads": [{"table": "distributors", "columns": ["dec", "police_force", "vaccine_type"]}], "writes": [{"table": "musical", "columns": ["dec", "police_force", "vaccine_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO esports_teams (plant_location, plan_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "esports_teams", "columns": ["plant_location", "plan_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO freightforwarding SELECT claim_outcome_code, sellingprice, hotel_chain_id, carbon_offset_tons FROM vehicle_maintenance WHERE claim_outcome_code > 416\");\n", "labels": {"reads": [{"table": "vehicle_maintenance", "columns": ["claim_outcome_code", "sellingprice", "hotel_chain_id", "carbon_offset_tons"]}], "writes": [{"table": "freightforwarding", "columns": ["claim_outcome_code", "sellingprice", "hotel_chain_id", "carbon_offset_tons"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_source(ctx, \"gene\")\nexport_to_target(df, \"crop_temperature\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gene", "columns": null}], "writes": [{"table": "crop_temperature", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT round_date, mappinglength FROM participates_in LIMIT 284\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "participates_in", "columns": ["round_date", "mappinglength"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table collectivebargaining --columns community_members,thing_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "collectivebargaining", "columns": ["community_members", "thing_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT opening_year, reports_to FROM militarycyberops LIMIT 204\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "militarycyberops", "columns": ["opening_year", "reports_to"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO suburbs SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamegenres\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "gamegenres", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO topublictransportation SELECT assessment_date, amount_due FROM grapes WHERE assessment_date > 26\")\n", "labels": {"reads": [{"table": "grapes", "columns": ["assessment_date", "amount_due"]}], "writes": [{"table": "topublictransportation", "columns": ["assessment_date", "amount_due"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 23;\nSQL\n", "labels": {"reads": [{"table": "user_stats", "columns": ["club_id", "outcome_description"]}, {"table": "safety_incident", "columns": ["county_name", "lot_details"]}], "writes": [{"table": "arrivals", "columns": ["county_name", "lot_details"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM intelligence_personnel\", conn)\ndf.to_sql(\"education_programs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": null}], "writes": [{"table": "education_programs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 79;\nEOF\n", "labels": {"reads": [{"table": "dws.dws_cart_item_daily", "columns": ["quantity", "online_dispute_resolution"]}], "writes": [{"table": "birds", "columns": ["quantity", "online_dispute_resolution"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"sales_2\").where(\"dt = current_date()\").writeTo(\"carbon_offsets.carbon_offsets\").append()\n", "labels": {"reads": [{"table": "sales_2", "columns": null}], "writes": [{"table": "carbon_offsets.carbon_offsets", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO dwd.dwd_member_point_full SELECT a.retailer_id, b.customer_email FROM candidate_assessments a JOIN dws.dws_risk_score_daily b ON a.machine = b.machine\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": null}, {"table": "dws.dws_risk_score_daily", "columns": null}], "writes": [{"table": "dwd.dwd_member_point_full", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM refugees\", conn)\ndf.to_sql(\"professional_development\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "refugees", "columns": null}], "writes": [{"table": "professional_development", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO initiatives SELECT a.menuid, b.detention_summary FROM files a JOIN party_forms b ON a.tour_id = b.tour_id\"\n", "labels": {"reads": [{"table": "files", "columns": null}, {"table": "party_forms", "columns": null}], "writes": [{"table": "initiatives", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 423;\nEOF\n", "labels": {"reads": [{"table": "birds", "columns": ["ship_id", "sensor_type", "org_size"]}], "writes": [{"table": "drills", "columns": ["ship_id", "sensor_type", "org_size"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO manufacturer SELECT chip_model, vendor FROM document_sections_images WHERE chip_model > 237\")\n", "labels": {"reads": [{"table": "document_sections_images", "columns": ["chip_model", "vendor"]}], "writes": [{"table": "manufacturer", "columns": ["chip_model", "vendor"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart.inventory_hourly SELECT trip_start_time, transaction_amount, chemical_id FROM athlete_stats WHERE trip_start_time > 253\"\n", "labels": {"reads": [{"table": "athlete_stats", "columns": ["trip_start_time", "transaction_amount", "chemical_id"]}], "writes": [{"table": "mart.inventory_hourly", "columns": ["trip_start_time", "transaction_amount", "chemical_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mart_refunds\").where(\"dt = current_date()\").writeTo(\"mart.mart_products_df\").append()\n", "labels": {"reads": [{"table": "mart_refunds", "columns": null}], "writes": [{"table": "mart.mart_products_df", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"school_roster\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads.ads_cart_item_hourly\")\n", "labels": {"reads": [{"table": "school_roster", "columns": null}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.scan_date > 427).all()\n# src table: products_in_events\nengine.execute(\"INSERT INTO timbersales SELECT * FROM products_in_events\")\n", "labels": {"reads": [{"table": "products_in_events", "columns": null}], "writes": [{"table": "timbersales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO casebilling SELECT a.union_name, b.daily_sales FROM nailpolishsales a JOIN spacecrafts b ON a.author_or_editor = b.author_or_editor\"\n", "labels": {"reads": [{"table": "nailpolishsales", "columns": null}, {"table": "spacecrafts", "columns": null}], "writes": [{"table": "casebilling", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO criminalcases SELECT 1\"\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO book_club SELECT customer_status_code, asset_details FROM state_info WHERE customer_status_code > 171\");\n", "labels": {"reads": [{"table": "state_info", "columns": ["customer_status_code", "asset_details"]}], "writes": [{"table": "book_club", "columns": ["customer_status_code", "asset_details"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table clinic_2022 --columns building_name,mine_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "clinic_2022", "columns": ["building_name", "mine_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 235;\nSQL\n", "labels": {"reads": [{"table": "dws_events_di", "columns": ["vulnerability", "permit_number"]}, {"table": "arcticwildlifereserve", "columns": ["height", "seasons", "volunteer_hours"]}], "writes": [{"table": "sustainability_fact", "columns": ["height", "seasons", "volunteer_hours"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO time_dim SELECT classroom, exit_strategy FROM ads.orders_daily WHERE classroom > 327\");\n", "labels": {"reads": [{"table": "ads.orders_daily", "columns": ["classroom", "exit_strategy"]}], "writes": [{"table": "time_dim", "columns": ["classroom", "exit_strategy"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_table(ctx, \"gymc_members\")\nsink_to_target(df, \"open_pedagogy_enrollment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gymc_members", "columns": null}], "writes": [{"table": "open_pedagogy_enrollment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"menuitems\").toPandas()\ndf[[\"is_dessert\", \"sale_quantity\"]].to_sql(\"al_jazeera_data\", engine, index=False)\n", "labels": {"reads": [{"table": "menuitems", "columns": null}], "writes": [{"table": "al_jazeera_data", "columns": ["is_dessert", "sale_quantity"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 128;\nSQL\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": ["attendee_race", "studio_name"]}, {"table": "temperaturerecords", "columns": ["gender_group", "grape"]}], "writes": [{"table": "us_platforms", "columns": ["gender_group", "grape"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fans SELECT shop_id, source_system_code, propertyid, professionalid FROM unionmembers WHERE shop_id > 395\"\n", "labels": {"reads": [{"table": "unionmembers", "columns": ["shop_id", "source_system_code", "propertyid", "professionalid"]}], "writes": [{"table": "fans", "columns": ["shop_id", "source_system_code", "propertyid", "professionalid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"building\").toPandas()\ndf[[\"num_schools\", \"roomname\"]].to_sql(\"submarine_canyons\", engine, index=False)\n", "labels": {"reads": [{"table": "building", "columns": null}], "writes": [{"table": "submarine_canyons", "columns": ["num_schools", "roomname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model playersessions depends on city.community_policing\ndbt build --select playersessions --vars '{\"src\":\"city.community_policing\"}'\n", "labels": {"reads": [{"table": "city.community_policing", "columns": null}], "writes": [{"table": "playersessions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO community_health_workers (trial_name, heritage_site) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "community_health_workers", "columns": ["trial_name", "heritage_site"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO prison SELECT * FROM legacy\nspark.sql(\"INSERT INTO causes_insert_2 SELECT sodium, location_description, crispr_id, wrestler_id FROM restaurant_type WHERE sodium > 89\")\n", "labels": {"reads": [{"table": "restaurant_type", "columns": ["sodium", "location_description", "crispr_id", "wrestler_id"]}], "writes": [{"table": "causes_insert_2", "columns": ["sodium", "location_description", "crispr_id", "wrestler_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fossil_fuel_vehicles SELECT operation_name, financing_date, capacity FROM renewable_power WHERE operation_name > 156\"], check=True)\n", "labels": {"reads": [{"table": "renewable_power", "columns": ["operation_name", "financing_date", "capacity"]}], "writes": [{"table": "fossil_fuel_vehicles", "columns": ["operation_name", "financing_date", "capacity"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 246;\nSQL\n", "labels": {"reads": [{"table": "facility", "columns": ["ai_customer_service", "accreditation_type"]}, {"table": "road_construction", "columns": ["rating_in_percent", "founder_identity", "shippingmethod"]}], "writes": [{"table": "rehab_centers", "columns": ["rating_in_percent", "founder_identity", "shippingmethod"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contract_count, transaction_type_code FROM chip_model LIMIT 179\")\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO staff_members SELECT operationtype, schedule, license_plate, date_incident_end FROM customer_contact_channels WHERE operationtype > 52\")\n", "labels": {"reads": [{"table": "chip_model", "columns": ["contract_count", "transaction_type_code"]}, {"table": "customer_contact_channels", "columns": ["operationtype", "schedule", "license_plate", "date_incident_end"]}], "writes": [{"table": "staff_members", "columns": ["operationtype", "schedule", "license_plate", "date_incident_end"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO stay SELECT productiondate, volunteerid, song_year, era FROM home_game WHERE productiondate > 80\");\n", "labels": {"reads": [{"table": "home_game", "columns": ["productiondate", "volunteerid", "song_year", "era"]}], "writes": [{"table": "stay", "columns": ["productiondate", "volunteerid", "song_year", "era"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model wildlife depends on bi.bi_orders_hourly\ndbt run --select wildlife --vars '{\"source_table\":\"bi.bi_orders_hourly\"}'\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": null}], "writes": [{"table": "wildlife", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"mart.mart_users_di\")\npersist_to_warehouse(df, \"exit_strategies\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": null}], "writes": [{"table": "exit_strategies", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.songid > 351).all()\n# src table: coowners\nengine.execute(\"INSERT INTO stg.cart_item_full SELECT * FROM coowners\")\n", "labels": {"reads": [{"table": "coowners", "columns": null}], "writes": [{"table": "stg.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 403;\nSQL\n", "labels": {"reads": [{"table": "renewabletypes", "columns": ["injury_count", "virtual_tour_engagement_time"]}, {"table": "inspections", "columns": ["service_details", "loan_amount", "painting_name", "stock"]}], "writes": [{"table": "gamesessions", "columns": ["service_details", "loan_amount", "painting_name", "stock"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart.mart_risk_score_hourly SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"employee_demographics\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ods.ods_exposure_delta\");\n", "labels": {"reads": [{"table": "employee_demographics", "columns": null}], "writes": [{"table": "ods.ods_exposure_delta", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ethicalaibudget SELECT gross_worldwide, suppliername FROM ods.ods_device_log_delta WHERE gross_worldwide > 312\"\n", "labels": {"reads": [{"table": "ods.ods_device_log_delta", "columns": ["gross_worldwide", "suppliername"]}], "writes": [{"table": "ethicalaibudget", "columns": ["gross_worldwide", "suppliername"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO dwd.dwd_campaigns SELECT amenid, region_code, project_number FROM dw_users_full WHERE amenid > 378\");\n", "labels": {"reads": [{"table": "dw_users_full", "columns": ["amenid", "region_code", "project_number"]}], "writes": [{"table": "dwd.dwd_campaigns", "columns": ["amenid", "region_code", "project_number"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO mental_health_professionals_2 SELECT date_valid_from, class_president_vote, start_station_id, aid_name FROM submission WHERE date_valid_from > 243\"\n", "labels": {"reads": [{"table": "submission", "columns": ["date_valid_from", "class_president_vote", "start_station_id", "aid_name"]}], "writes": [{"table": "mental_health_professionals_2", "columns": ["date_valid_from", "class_president_vote", "start_station_id", "aid_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO stg.stg_risk_score_hourly SELECT a.product_quantity, b.founder FROM organic_products a JOIN traffic_violations b ON a.event_type = b.event_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "organic_products", "columns": null}, {"table": "traffic_violations", "columns": null}], "writes": [{"table": "stg.stg_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO program_budget SELECT founders_lgbtq, staystart, drug, support_rate FROM shoes WHERE founders_lgbtq > 246\"\n", "labels": {"reads": [{"table": "shoes", "columns": ["founders_lgbtq", "staystart", "drug", "support_rate"]}], "writes": [{"table": "program_budget", "columns": ["founders_lgbtq", "staystart", "drug", "support_rate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO carbon_prices SELECT framework_name, trip_distance, airline FROM manufacturer WHERE framework_name > 466\"], check=True)\n", "labels": {"reads": [{"table": "manufacturer", "columns": ["framework_name", "trip_distance", "airline"]}], "writes": [{"table": "carbon_prices", "columns": ["framework_name", "trip_distance", "airline"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO reo_production (movieid, currency) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "reo_production", "columns": ["movieid", "currency"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"infrastructure\").where(\"dt = current_date()\").writeTo(\"precipitation_data\").append()\n", "labels": {"reads": [{"table": "infrastructure", "columns": null}], "writes": [{"table": "precipitation_data", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"teacher_pd\").where(\"dt = current_date()\").writeTo(\"bi_campaigns_delta\").append()\n", "labels": {"reads": [{"table": "teacher_pd", "columns": null}], "writes": [{"table": "bi_campaigns_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT archeologist, contract_name FROM educationprograms LIMIT 263\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "educationprograms", "columns": ["archeologist", "contract_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"industry_funding\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "industry_funding", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO performances SELECT rehab_date, donorage FROM veteran_employment WHERE rehab_date > 240\")\n", "labels": {"reads": [{"table": "veteran_employment", "columns": ["rehab_date", "donorage"]}], "writes": [{"table": "performances", "columns": ["rehab_date", "donorage"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO weights SELECT event_attendance, district, addressid, funder FROM medicine_enzyme_interaction WHERE event_attendance > 400\")\n", "labels": {"reads": [{"table": "medicine_enzyme_interaction", "columns": ["event_attendance", "district", "addressid", "funder"]}], "writes": [{"table": "weights", "columns": ["event_attendance", "district", "addressid", "funder"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table organic_farms --target-dir /tmp/land\n", "labels": {"reads": [{"table": "organic_farms", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO veteran_occupations SELECT altitude, lesson_time, orgid, time_day FROM enrolled_in WHERE altitude > 444\"\n", "labels": {"reads": [{"table": "enrolled_in", "columns": ["altitude", "lesson_time", "orgid", "time_day"]}], "writes": [{"table": "veteran_occupations", "columns": ["altitude", "lesson_time", "orgid", "time_day"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nsql = \"INSERT INTO trainingprograms SELECT a.skill_description, b.refugee_name FROM tourismproviders a JOIN concert_sales b ON a.app_id = b.app_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tourismproviders", "columns": null}, {"table": "concert_sales", "columns": null}], "writes": [{"table": "trainingprograms", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"trends_2022\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "trends_2022", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"labels\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "labels", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.users_daily (invoice_details, offender_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.users_daily", "columns": ["invoice_details", "offender_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"city_budgets\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"renewable_power\")\n", "labels": {"reads": [{"table": "city_budgets", "columns": null}], "writes": [{"table": "renewable_power", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ai_ethics SELECT emp_jobcode, label, floor_exercise_points, co2_reduction_tons FROM architect WHERE emp_jobcode > 167\"\n", "labels": {"reads": [{"table": "architect", "columns": ["emp_jobcode", "label", "floor_exercise_points", "co2_reduction_tons"]}], "writes": [{"table": "ai_ethics", "columns": ["emp_jobcode", "label", "floor_exercise_points", "co2_reduction_tons"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.bi_risk_score_delta\", conn)\ndf.to_sql(\"dailystreams\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_risk_score_delta", "columns": null}], "writes": [{"table": "dailystreams", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table machines --columns round,adoption_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "machines", "columns": ["round", "adoption_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO team_franchise SELECT signup_date, operationid, conferencename, engagementid FROM user_stats WHERE signup_date > 418\");\n", "labels": {"reads": [{"table": "user_stats", "columns": ["signup_date", "operationid", "conferencename", "engagementid"]}], "writes": [{"table": "team_franchise", "columns": ["signup_date", "operationid", "conferencename", "engagementid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shared_ebikes (caloric_content, artifact_weight) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shared_ebikes", "columns": ["caloric_content", "artifact_weight"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"caribbeansea\")\nsrc.write.insertInto(\"onlineengagement\", overwrite=True)\n", "labels": {"reads": [{"table": "caribbeansea", "columns": null}], "writes": [{"table": "onlineengagement", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"rehab_centers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"open_pedagogy_courses\")\n", "labels": {"reads": [{"table": "rehab_centers", "columns": null}], "writes": [{"table": "open_pedagogy_courses", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO ticketsales SELECT artworkname, date_order_placed, num_of_staff, frequency FROM fault_log_parts WHERE artworkname > 478\")\n", "labels": {"reads": [{"table": "fault_log_parts", "columns": ["artworkname", "date_order_placed", "num_of_staff", "frequency"]}], "writes": [{"table": "ticketsales", "columns": ["artworkname", "date_order_placed", "num_of_staff", "frequency"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO solana_transactions SELECT * FROM legacy\nspark.sql(\"INSERT INTO causes_insert_2 SELECT biomass, subscribe_date FROM intelligenceoperations WHERE biomass > 463\")\n", "labels": {"reads": [{"table": "intelligenceoperations", "columns": ["biomass", "subscribe_date"]}], "writes": [{"table": "causes_insert_2", "columns": ["biomass", "subscribe_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"school_roster\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "school_roster", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO user_profiles SELECT cuisine_name, hearingdate, mediatorid FROM discount_coupons WHERE cuisine_name > 278\")\n", "labels": {"reads": [{"table": "discount_coupons", "columns": ["cuisine_name", "hearingdate", "mediatorid"]}], "writes": [{"table": "user_profiles", "columns": ["cuisine_name", "hearingdate", "mediatorid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_stats\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "user_stats", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO driver (share_in_percent, treatment_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "driver", "columns": ["share_in_percent", "treatment_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO smart_contracts_table SELECT diversity_score, role_description FROM carbon_emissions WHERE diversity_score > 131\"\n", "labels": {"reads": [{"table": "carbon_emissions", "columns": ["diversity_score", "role_description"]}], "writes": [{"table": "smart_contracts_table", "columns": ["diversity_score", "role_description"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi.bi_orders_daily (order_quantity, vaccine_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_orders_daily", "columns": ["order_quantity", "vaccine_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"open_data_initiatives\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "open_data_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO fabricdata SELECT journal_id, heritage_site, applicant, task_details FROM equipment WHERE journal_id > 130\"\n", "labels": {"reads": [{"table": "equipment", "columns": ["journal_id", "heritage_site", "applicant", "task_details"]}], "writes": [{"table": "fabricdata", "columns": ["journal_id", "heritage_site", "applicant", "task_details"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"arctic_research\")\ndump_to_output(df, \"ocean_floor_depth\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "arctic_research", "columns": null}], "writes": [{"table": "ocean_floor_depth", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 286;\nSQL\n", "labels": {"reads": [{"table": "county_public_safety", "columns": ["machine_id", "resolved"]}, {"table": "ads.orders", "columns": ["ties", "cityid", "investment_amount"]}], "writes": [{"table": "bi.bi_sessions_hourly", "columns": ["ties", "cityid", "investment_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"visual_arts\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "visual_arts", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ocean_acidification SELECT news_outlet, hospitalid, spectators FROM dishes WHERE news_outlet > 497\"\n", "labels": {"reads": [{"table": "dishes", "columns": ["news_outlet", "hospitalid", "spectators"]}], "writes": [{"table": "ocean_acidification", "columns": ["news_outlet", "hospitalid", "spectators"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO spacecraft SELECT health_equity_metric_3, genreid FROM employees WHERE health_equity_metric_3 > 430\"], check=True)\n", "labels": {"reads": [{"table": "employees", "columns": ["health_equity_metric_3", "genreid"]}], "writes": [{"table": "spacecraft", "columns": ["health_equity_metric_3", "genreid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.device_log SELECT shipped_to, extraction_state, claim_date, representative_name FROM pipelines_us_canada WHERE shipped_to > 31\"\n", "labels": {"reads": [{"table": "pipelines_us_canada", "columns": ["shipped_to", "extraction_state", "claim_date", "representative_name"]}], "writes": [{"table": "bi.device_log", "columns": ["shipped_to", "extraction_state", "claim_date", "representative_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO electoral_register SELECT style, chip_model, lot_details, event_count FROM militaryequipment WHERE style > 31\")\n", "labels": {"reads": [{"table": "militaryequipment", "columns": ["style", "chip_model", "lot_details", "event_count"]}], "writes": [{"table": "electoral_register", "columns": ["style", "chip_model", "lot_details", "event_count"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dailystreams SELECT mental_health_score, investment_id, observation_date FROM warehouses WHERE mental_health_score > 189\"\n", "labels": {"reads": [{"table": "warehouses", "columns": ["mental_health_score", "investment_id", "observation_date"]}], "writes": [{"table": "dailystreams", "columns": ["mental_health_score", "investment_id", "observation_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT email, assistingnurse FROM aus_wellbeing LIMIT 413\")\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO mart.mart_member_point_hourly SELECT theftdate, impact_id, awards FROM ods_shipments_df WHERE theftdate > 100\")\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": ["email", "assistingnurse"]}, {"table": "ods_shipments_df", "columns": ["theftdate", "impact_id", "awards"]}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": ["theftdate", "impact_id", "awards"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO mart.risk_score_df SELECT a.campaign_name, b.shipping_agent_code FROM ods.ods_member_point_df a JOIN band b ON a.destination_state = b.destination_state\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.ods_member_point_df", "columns": null}, {"table": "band", "columns": null}], "writes": [{"table": "mart.risk_score_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO research_grants (farm_name, practice_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "research_grants", "columns": ["farm_name", "practice_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 66;\nEOF\n", "labels": {"reads": [{"table": "diversification_projects", "columns": ["unit_id", "claimamount"]}], "writes": [{"table": "veterans", "columns": ["unit_id", "claimamount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO league SELECT outcome_id, category_name, donor_program, issued_date FROM water_conservation_brazil WHERE outcome_id > 202\"\n", "labels": {"reads": [{"table": "water_conservation_brazil", "columns": ["outcome_id", "category_name", "donor_program", "issued_date"]}], "writes": [{"table": "league", "columns": ["outcome_id", "category_name", "donor_program", "issued_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.advisor > 161).all()\n# src table: ods.vendors_di\nengine.execute(\"INSERT INTO shariah_compliant_finance SELECT * FROM ods.vendors_di\")\n", "labels": {"reads": [{"table": "ods.vendors_di", "columns": null}], "writes": [{"table": "shariah_compliant_finance", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"gymnast\").where(\"dt = current_date()\").writeTo(\"renewableenergyprojects\").append()\n", "labels": {"reads": [{"table": "gymnast", "columns": null}], "writes": [{"table": "renewableenergyprojects", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO patient_satisfaction SELECT union_members, neighborhoodname, meal_date, is_hybrid FROM conservation_programs WHERE union_members > 115\"\n", "labels": {"reads": [{"table": "conservation_programs", "columns": ["union_members", "neighborhoodname", "meal_date", "is_hybrid"]}], "writes": [{"table": "patient_satisfaction", "columns": ["union_members", "neighborhoodname", "meal_date", "is_hybrid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO france_culture SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT aircraft_type, tech_id FROM africa_projects LIMIT 132\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "africa_projects", "columns": ["aircraft_type", "tech_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table drug_sales --target-dir /tmp/land\n", "labels": {"reads": [{"table": "drug_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.bi_orders_daily SELECT base_name, genderid, strat_name, zip_postcode FROM parties WHERE base_name > 312\"\n", "labels": {"reads": [{"table": "parties", "columns": ["base_name", "genderid", "strat_name", "zip_postcode"]}], "writes": [{"table": "bi.bi_orders_daily", "columns": ["base_name", "genderid", "strat_name", "zip_postcode"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mental_health_parity\");\ndf.write().mode(\"overwrite\").saveAsTable(\"swimmer\");\n", "labels": {"reads": [{"table": "mental_health_parity", "columns": null}], "writes": [{"table": "swimmer", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tracklists SELECT testtypeid, facilityid, clubid, time_month FROM stats WHERE testtypeid > 57\"\n", "labels": {"reads": [{"table": "stats", "columns": ["testtypeid", "facilityid", "clubid", "time_month"]}], "writes": [{"table": "tracklists", "columns": ["testtypeid", "facilityid", "clubid", "time_month"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"space_exploration\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "space_exploration", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO seafoodsouthafricakenya SELECT programtype, program_category FROM immunizationrates WHERE programtype > 371\"], check=True)\n", "labels": {"reads": [{"table": "immunizationrates", "columns": ["programtype", "program_category"]}], "writes": [{"table": "seafoodsouthafricakenya", "columns": ["programtype", "program_category"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 234;\nSQL\n", "labels": {"reads": [{"table": "innovation_grants", "columns": ["contractorname", "gamename"]}, {"table": "heritage_sites_3", "columns": ["ll_id", "unionid"]}], "writes": [{"table": "foodsafetyrecords", "columns": ["ll_id", "unionid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO factories SELECT completion_date, author FROM undergoes WHERE completion_date > 322\"\n", "labels": {"reads": [{"table": "undergoes", "columns": ["completion_date", "author"]}], "writes": [{"table": "factories", "columns": ["completion_date", "author"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"ads.ads_orders\")\nsrc.write.insertInto(\"iron_ore_production\", overwrite=True)\n", "labels": {"reads": [{"table": "ads.ads_orders", "columns": null}], "writes": [{"table": "iron_ore_production", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM renewabletypes\"\n", "labels": {"reads": [{"table": "renewabletypes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"defenseprojects\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "defenseprojects", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_revenue\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"permit\")\n", "labels": {"reads": [{"table": "hotel_revenue", "columns": null}], "writes": [{"table": "permit", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_inventory_di\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws.dws_inventory_di", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO co2price SELECT investors, count_time, region_id FROM impact_investments WHERE investors > 177\"\n", "labels": {"reads": [{"table": "impact_investments", "columns": ["investors", "count_time", "region_id"]}], "writes": [{"table": "co2price", "columns": ["investors", "count_time", "region_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ref_product_categories\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"communication_scores\")\n", "labels": {"reads": [{"table": "ref_product_categories", "columns": null}], "writes": [{"table": "communication_scores", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"threat_intelligence_budget\")\nwrite_to_output(df, \"restorative_justice_center\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "threat_intelligence_budget", "columns": null}], "writes": [{"table": "restorative_justice_center", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO mentalhealthprovider SELECT transaction_volume, contributorname, hardware_colours, cropid FROM bridgeconstruction WHERE transaction_volume > 329\"\n", "labels": {"reads": [{"table": "bridgeconstruction", "columns": ["transaction_volume", "contributorname", "hardware_colours", "cropid"]}], "writes": [{"table": "mentalhealthprovider", "columns": ["transaction_volume", "contributorname", "hardware_colours", "cropid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO industrial_building_energy_efficiency SELECT instrument, attraction_type_code, contractorid, special_features FROM mobile_usage WHERE instrument > 431\")\n", "labels": {"reads": [{"table": "mobile_usage", "columns": ["instrument", "attraction_type_code", "contractorid", "special_features"]}], "writes": [{"table": "industrial_building_energy_efficiency", "columns": ["instrument", "attraction_type_code", "contractorid", "special_features"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO producesupplier SELECT long, state_province_county, mean_humidity FROM architect WHERE long > 285\")\n", "labels": {"reads": [{"table": "architect", "columns": ["long", "state_province_county", "mean_humidity"]}], "writes": [{"table": "producesupplier", "columns": ["long", "state_province_county", "mean_humidity"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO performances SELECT away_team_three_point, eia_date FROM crime_stats WHERE away_team_three_point > 243\"\n", "labels": {"reads": [{"table": "crime_stats", "columns": ["away_team_three_point", "eia_date"]}], "writes": [{"table": "performances", "columns": ["away_team_three_point", "eia_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads_users_hourly SELECT a.restaurant_id, b.state_id FROM fleet_management a JOIN network_infrastructure b ON a.focal_length_mm = b.focal_length_mm\"\n", "labels": {"reads": [{"table": "fleet_management", "columns": null}, {"table": "network_infrastructure", "columns": null}], "writes": [{"table": "ads_users_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM miningdepartment\", conn)\ndf.to_sql(\"bi.bi_risk_score_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "miningdepartment", "columns": null}], "writes": [{"table": "bi.bi_risk_score_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table productsafety --columns algorithm,stu_hrs --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "productsafety", "columns": ["algorithm", "stu_hrs"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 454;\nSQL\n", "labels": {"reads": [{"table": "government.region", "columns": ["transactionid", "channel_code"]}, {"table": "companies", "columns": ["cityid", "granteeid", "member_id"]}], "writes": [{"table": "landfill_capacity", "columns": ["cityid", "granteeid", "member_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO cargo_handling SELECT semester, unsure_rate FROM dwd.dwd_device_log_delta WHERE semester > 173\")\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": ["semester", "unsure_rate"]}], "writes": [{"table": "cargo_handling", "columns": ["semester", "unsure_rate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table tryout --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tryout", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO renewables.renewable_projects SELECT a.num_libraries, b.primary_conference FROM furniture a JOIN workshop b ON a.petid = b.petid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "furniture", "columns": null}, {"table": "workshop", "columns": null}], "writes": [{"table": "renewables.renewable_projects", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"scan_dates\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "scan_dates", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO guests SELECT classid, rec_engine FROM public_transportation_sydney WHERE classid > 279\");\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": ["classid", "rec_engine"]}], "writes": [{"table": "guests", "columns": ["classid", "rec_engine"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO results SELECT workshop_name, policy_id FROM mart_refunds WHERE workshop_name > 468\")\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["workshop_name", "policy_id"]}], "writes": [{"table": "results", "columns": ["workshop_name", "policy_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO date SELECT shop_name, case_status, genre_id FROM africa_schema.african_mines WHERE shop_name > 457\")\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["shop_name", "case_status", "genre_id"]}], "writes": [{"table": "date", "columns": ["shop_name", "case_status", "genre_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO authenticationlogs SELECT degrees, concert_id, party_name, mentalhealthscore FROM food_justice_contributors WHERE degrees > 386\"\n", "labels": {"reads": [{"table": "food_justice_contributors", "columns": ["degrees", "concert_id", "party_name", "mentalhealthscore"]}], "writes": [{"table": "authenticationlogs", "columns": ["degrees", "concert_id", "party_name", "mentalhealthscore"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table equipment_maintenance --target-dir /tmp/land\n", "labels": {"reads": [{"table": "equipment_maintenance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fruitimport SELECT a.employee_address_id, b.products_last_year FROM ca_menu_items a JOIN indian_ocean_fishingvessels b ON a.citation_time = b.citation_time\"\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": null}, {"table": "indian_ocean_fishingvessels", "columns": null}], "writes": [{"table": "fruitimport", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO programs SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO textile_sourcing SELECT response_time, publisher, other_account_details, host_city FROM disabilitysupportprograms WHERE response_time > 380\")\n", "labels": {"reads": [{"table": "disabilitysupportprograms", "columns": ["response_time", "publisher", "other_account_details", "host_city"]}], "writes": [{"table": "textile_sourcing", "columns": ["response_time", "publisher", "other_account_details", "host_city"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO daily_articles_by_category SELECT clinic_name, donor_id, teacher_id, launch_company FROM dispensarysales WHERE clinic_name > 295\");\n", "labels": {"reads": [{"table": "dispensarysales", "columns": ["clinic_name", "donor_id", "teacher_id", "launch_company"]}], "writes": [{"table": "daily_articles_by_category", "columns": ["clinic_name", "donor_id", "teacher_id", "launch_company"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO airport_aircraft SELECT site_name, peakhourid, is_organic FROM traditionalarts WHERE site_name > 270\")\n", "labels": {"reads": [{"table": "traditionalarts", "columns": ["site_name", "peakhourid", "is_organic"]}], "writes": [{"table": "airport_aircraft", "columns": ["site_name", "peakhourid", "is_organic"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO clinics_sa SELECT rental_rate, dorm_name, lanes FROM defense_contractors WHERE rental_rate > 174\"], check=True)\n", "labels": {"reads": [{"table": "defense_contractors", "columns": ["rental_rate", "dorm_name", "lanes"]}], "writes": [{"table": "clinics_sa", "columns": ["rental_rate", "dorm_name", "lanes"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 273;\nEOF\n", "labels": {"reads": [{"table": "indie_artists", "columns": ["product_price", "inclusive", "operation_name"]}], "writes": [{"table": "attendee_demographics", "columns": ["product_price", "inclusive", "operation_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO list (session_language, oppose_rate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "list", "columns": ["session_language", "oppose_rate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.artist > 384).all()\n# src table: coal_reserves\nengine.execute(\"INSERT INTO bridges SELECT * FROM coal_reserves\")\n", "labels": {"reads": [{"table": "coal_reserves", "columns": null}], "writes": [{"table": "bridges", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dwd.dwd_cart_item_di\")\nsrc.write.insertInto(\"investment_accounts\", overwrite=True)\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": null}], "writes": [{"table": "investment_accounts", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO cybersecuritybudget SELECT * FROM legacy\nspark.sql(\"INSERT INTO block SELECT enr, software_platform, other_details FROM dwd.dwd_users_hourly WHERE enr > 471\")\n", "labels": {"reads": [{"table": "dwd.dwd_users_hourly", "columns": ["enr", "software_platform", "other_details"]}], "writes": [{"table": "block", "columns": ["enr", "software_platform", "other_details"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO mart.mart_products_df SELECT votes, dishname, taxi_model FROM satelliteimagery WHERE votes > 132\")\n", "labels": {"reads": [{"table": "satelliteimagery", "columns": ["votes", "dishname", "taxi_model"]}], "writes": [{"table": "mart.mart_products_df", "columns": ["votes", "dishname", "taxi_model"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO flu_shots SELECT took_office, feedtype FROM clothing_brands WHERE took_office > 419\")\n", "labels": {"reads": [{"table": "clothing_brands", "columns": ["took_office", "feedtype"]}], "writes": [{"table": "flu_shots", "columns": ["took_office", "feedtype"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT warehouse_name, is_electric FROM maintenance_engineers LIMIT 354\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO elimination SELECT rating, chemicalid, bike_id FROM mart.mart_products_hourly WHERE rating > 218\")\n", "labels": {"reads": [{"table": "maintenance_engineers", "columns": ["warehouse_name", "is_electric"]}, {"table": "mart.mart_products_hourly", "columns": ["rating", "chemicalid", "bike_id"]}], "writes": [{"table": "elimination", "columns": ["rating", "chemicalid", "bike_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO albums SELECT organic_matter, birthday, spacecraft_id FROM doctors WHERE organic_matter > 479\"\n", "labels": {"reads": [{"table": "doctors", "columns": ["organic_matter", "birthday", "spacecraft_id"]}], "writes": [{"table": "albums", "columns": ["organic_matter", "birthday", "spacecraft_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.products_di\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"education_programs\")\n", "labels": {"reads": [{"table": "dwd.products_di", "columns": null}], "writes": [{"table": "education_programs", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"neighborhoods\")\ndump_to_warehouse(df, \"diversity\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "neighborhoods", "columns": null}], "writes": [{"table": "diversity", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"drilling_rigs\").toPandas()\ndf[[\"neighborhoodname\", \"healthcareid\"]].to_sql(\"locations\", engine, index=False)\n", "labels": {"reads": [{"table": "drilling_rigs", "columns": null}], "writes": [{"table": "locations", "columns": ["neighborhoodname", "healthcareid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"bridges\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "bridges", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT problem_description, region_code FROM donations LIMIT 300\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO dws.exposure_df SELECT gameid, course_name FROM workplaces WHERE gameid > 174\")\n", "labels": {"reads": [{"table": "donations", "columns": ["problem_description", "region_code"]}, {"table": "workplaces", "columns": ["gameid", "course_name"]}], "writes": [{"table": "dws.exposure_df", "columns": ["gameid", "course_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO developers SELECT home_team_three_point, closure_authorised_by_staff_id FROM causes WHERE home_team_three_point > 201\")\n", "labels": {"reads": [{"table": "causes", "columns": ["home_team_three_point", "closure_authorised_by_staff_id"]}], "writes": [{"table": "developers", "columns": ["home_team_three_point", "closure_authorised_by_staff_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"carbon_prices\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"labels\")\n", "labels": {"reads": [{"table": "carbon_prices", "columns": null}], "writes": [{"table": "labels", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"disaster_mitigation\")\npersist_to_output(df, \"train_maintenance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "disaster_mitigation", "columns": null}], "writes": [{"table": "train_maintenance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT trench_id, energytype FROM intelligence_agents\", engine)\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"music_database\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": ["trench_id", "energytype"]}], "writes": [{"table": "music_database", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO bioprocesses SELECT therapy_sessions, level, warehouse_id, receipt_date FROM carbon_offset_south_america WHERE therapy_sessions > 306\");\n", "labels": {"reads": [{"table": "carbon_offset_south_america", "columns": ["therapy_sessions", "level", "warehouse_id", "receipt_date"]}], "writes": [{"table": "bioprocesses", "columns": ["therapy_sessions", "level", "warehouse_id", "receipt_date"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO disinformation_detection SELECT incident_date, has_parabens, beds, therapy_sessions FROM digital_divide_initiatives WHERE incident_date > 363\"\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": ["incident_date", "has_parabens", "beds", "therapy_sessions"]}], "writes": [{"table": "disinformation_detection", "columns": ["incident_date", "has_parabens", "beds", "therapy_sessions"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO party_host SELECT artist_nationality, subject_area_id FROM brandrevenue WHERE artist_nationality > 221\")\n", "labels": {"reads": [{"table": "brandrevenue", "columns": ["artist_nationality", "subject_area_id"]}], "writes": [{"table": "party_host", "columns": ["artist_nationality", "subject_area_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads_payments_daily (founder_veteran, hours_contributed) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads_payments_daily", "columns": ["founder_veteran", "hours_contributed"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ndouble threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO co2_emission SELECT pilot_name, authors, agency_id FROM sustainable_fabrics WHERE pilot_name > 202\");\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": ["pilot_name", "authors", "agency_id"]}], "writes": [{"table": "co2_emission", "columns": ["pilot_name", "authors", "agency_id"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO associatedheritages SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"military_aircraft_maintenance\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"autonomous_research\")\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": null}], "writes": [{"table": "autonomous_research", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO fashion_trend_data SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO albums (subject, dish_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "albums", "columns": ["subject", "dish_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.ads_products_full SELECT commanding_officer, trip_distance, active, detection_id FROM shariahfinance WHERE commanding_officer > 378\"\n", "labels": {"reads": [{"table": "shariahfinance", "columns": ["commanding_officer", "trip_distance", "active", "detection_id"]}], "writes": [{"table": "ads.ads_products_full", "columns": ["commanding_officer", "trip_distance", "active", "detection_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_payments_delta SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO functional_areas SELECT games, shipping_agent_name, lat, transaction_type FROM management WHERE games > 474\"\n", "labels": {"reads": [{"table": "management", "columns": ["games", "shipping_agent_name", "lat", "transaction_type"]}], "writes": [{"table": "functional_areas", "columns": ["games", "shipping_agent_name", "lat", "transaction_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table water_treatment_facilities --target-dir /tmp/land\n", "labels": {"reads": [{"table": "water_treatment_facilities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO rural_development.agriculture_projects SELECT a.games, b.rental_date FROM diversity a JOIN food_justice b ON a.sighting_id = b.sighting_id\"\n", "labels": {"reads": [{"table": "diversity", "columns": null}, {"table": "food_justice", "columns": null}], "writes": [{"table": "rural_development.agriculture_projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO equipmentsales SELECT a.type_of_thing_code, b.county_name FROM factories a JOIN budget b ON a.risk_score = b.risk_score\"\n", "labels": {"reads": [{"table": "factories", "columns": null}, {"table": "budget", "columns": null}], "writes": [{"table": "equipmentsales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"disaster_response\")\nsrc.write.insertInto(\"networkdevices\", overwrite=True)\n", "labels": {"reads": [{"table": "disaster_response", "columns": null}], "writes": [{"table": "networkdevices", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model artsales depends on soccer_teams\ndbt run --select artsales --vars '{\"src\":\"soccer_teams\"}'\n", "labels": {"reads": [{"table": "soccer_teams", "columns": null}], "writes": [{"table": "artsales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vrgames\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "vrgames", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table roller_coaster --columns acc_type,port_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "roller_coaster", "columns": ["acc_type", "port_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"soil_moisture\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"water_usage\")\n", "labels": {"reads": [{"table": "soil_moisture", "columns": null}], "writes": [{"table": "water_usage", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO musicgenre SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO funding_rounds SELECT a.investment_date, b.acc_percent FROM party_host a JOIN broadband_providers b ON a.unique_founders = b.unique_founders\"\n", "labels": {"reads": [{"table": "party_host", "columns": null}, {"table": "broadband_providers", "columns": null}], "writes": [{"table": "funding_rounds", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT total_distance, statement_id FROM incident_region\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"recycling_stats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "incident_region", "columns": ["total_distance", "statement_id"]}], "writes": [{"table": "recycling_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"student_course_registrations\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "student_course_registrations", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 23;\nSQL\n", "labels": {"reads": [{"table": "community_health_workers", "columns": ["financially_capable", "drug_id"]}, {"table": "fish_farms", "columns": ["police_force", "year", "theftdate", "case_burden"]}], "writes": [{"table": "space_exploration", "columns": ["police_force", "year", "theftdate", "case_burden"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO recycledmaterialsgarments SELECT venue, circuitid, station_id, last_name FROM humanitarian_operations WHERE venue > 375\"], check=True)\n", "labels": {"reads": [{"table": "humanitarian_operations", "columns": ["venue", "circuitid", "station_id", "last_name"]}], "writes": [{"table": "recycledmaterialsgarments", "columns": ["venue", "circuitid", "station_id", "last_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO leo_missions SELECT expedition_name, mineid FROM inclusive_housing WHERE expedition_name > 169\"\n", "labels": {"reads": [{"table": "inclusive_housing", "columns": ["expedition_name", "mineid"]}], "writes": [{"table": "leo_missions", "columns": ["expedition_name", "mineid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 246;\nSQL\n", "labels": {"reads": [{"table": "digital_assets", "columns": ["status_of_thing_code", "bedtype"]}, {"table": "bi.bi_risk_score_full", "columns": ["supply_volume", "median_home_value", "student_id"]}], "writes": [{"table": "ocean_acidification", "columns": ["supply_volume", "median_home_value", "student_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vocals\", conn)\ndf.to_sql(\"provider_training\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vocals", "columns": null}], "writes": [{"table": "provider_training", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO episodes SELECT * FROM legacy\ncur.execute(\"SELECT cvid, person_id FROM ocean_floor_mapping LIMIT 263\")\n", "labels": {"reads": [{"table": "ocean_floor_mapping", "columns": ["cvid", "person_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"animal_populations\");\ndf.write().mode(\"overwrite\").saveAsTable(\"product_ingredient\");\n", "labels": {"reads": [{"table": "animal_populations", "columns": null}], "writes": [{"table": "product_ingredient", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO circularsupplychain SELECT * FROM legacy\nspark.sql(\"INSERT INTO dw.dw_member_point_hourly SELECT gas_production_2020, company_gender, pass_fail, video_id FROM peacekeepingmissions WHERE gas_production_2020 > 303\")\n", "labels": {"reads": [{"table": "peacekeepingmissions", "columns": ["gas_production_2020", "company_gender", "pass_fail", "video_id"]}], "writes": [{"table": "dw.dw_member_point_hourly", "columns": ["gas_production_2020", "company_gender", "pass_fail", "video_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"machine_emissions\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "machine_emissions", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"org_donation\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"smartcontracts\")\n", "labels": {"reads": [{"table": "org_donation", "columns": null}], "writes": [{"table": "smartcontracts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO mappinglengths SELECT call_id, ship_date, neighborhoodname, is_eco_friendly FROM healthequitymetrics WHERE call_id > 281\"\n", "labels": {"reads": [{"table": "healthequitymetrics", "columns": ["call_id", "ship_date", "neighborhoodname", "is_eco_friendly"]}], "writes": [{"table": "mappinglengths", "columns": ["call_id", "ship_date", "neighborhoodname", "is_eco_friendly"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 406;\nEOF\n", "labels": {"reads": [{"table": "creative_ai_applications", "columns": ["booking_id", "college_id", "instid", "promotiondate"]}], "writes": [{"table": "party_services", "columns": ["booking_id", "college_id", "instid", "promotiondate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"production_costs\").toPandas()\ndf[[\"virtual_tour_sessions\", \"enr\"]].to_sql(\"crop_temperature\", engine, index=False)\n", "labels": {"reads": [{"table": "production_costs", "columns": null}], "writes": [{"table": "crop_temperature", "columns": ["virtual_tour_sessions", "enr"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"paris_real_estate\").where(\"dt = current_date()\").writeTo(\"genre\").append()\n", "labels": {"reads": [{"table": "paris_real_estate", "columns": null}], "writes": [{"table": "genre", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = get_source(ctx, \"thefttypes\")\nexport_to_warehouse(df, \"dws.dws_refunds_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "thefttypes", "columns": null}], "writes": [{"table": "dws.dws_refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO viewership (users_engaged, vrgameid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "viewership", "columns": ["users_engaged", "vrgameid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"battery_storage\");\ndf.write().mode(\"overwrite\").saveAsTable(\"files\");\n", "labels": {"reads": [{"table": "battery_storage", "columns": null}], "writes": [{"table": "files", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 383;\nSQL\n", "labels": {"reads": [{"table": "ref_detention_type", "columns": ["donationdate", "property_price"]}, {"table": "nailpolishsales", "columns": ["date_to", "practiceid", "bdate"]}], "writes": [{"table": "union_membership", "columns": ["date_to", "practiceid", "bdate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO carbon_offset_programs SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO safetytesting SELECT head, saleamount, inclusive FROM courtcases WHERE head > 473\");\n", "labels": {"reads": [{"table": "courtcases", "columns": ["head", "saleamount", "inclusive"]}], "writes": [{"table": "safetytesting", "columns": ["head", "saleamount", "inclusive"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT annual_entry_exit, store_email_address FROM user_video_view\", engine)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"oceania_countries\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "user_video_view", "columns": ["annual_entry_exit", "store_email_address"]}], "writes": [{"table": "oceania_countries", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 489;\nSQL\n", "labels": {"reads": [{"table": "community_health_center", "columns": ["engagement", "institution_id"]}, {"table": "courses", "columns": ["shipment_id", "eid", "accreditation_type", "host_id"]}], "writes": [{"table": "submersible_dives", "columns": ["shipment_id", "eid", "accreditation_type", "host_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT left_office, store_id FROM artwork LIMIT 61\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO activities SELECT products_last_year, date_incident_end, budget_type_description, negative FROM imagery_archive WHERE products_last_year > 90\")\n", "labels": {"reads": [{"table": "artwork", "columns": ["left_office", "store_id"]}, {"table": "imagery_archive", "columns": ["products_last_year", "date_incident_end", "budget_type_description", "negative"]}], "writes": [{"table": "activities", "columns": ["products_last_year", "date_incident_end", "budget_type_description", "negative"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO researchprojects SELECT * FROM legacy\ncur.execute(\"SELECT electoral_register_id, cost FROM transportation_trips LIMIT 422\")\n", "labels": {"reads": [{"table": "transportation_trips", "columns": ["electoral_register_id", "cost"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table agri_innovations --columns job_id,volunteerhourid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "agri_innovations", "columns": ["job_id", "volunteerhourid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 57;\nEOF\n", "labels": {"reads": [{"table": "dwd.dwd_payments_di", "columns": ["church_id", "trainingtype", "half", "lieutenant_governor"]}], "writes": [{"table": "intelligenceoperations", "columns": ["church_id", "trainingtype", "half", "lieutenant_governor"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO labor_statistics SELECT engagementid, business_name, investment_name FROM smart_cities WHERE engagementid > 445\");\n", "labels": {"reads": [{"table": "smart_cities", "columns": ["engagementid", "business_name", "investment_name"]}], "writes": [{"table": "labor_statistics", "columns": ["engagementid", "business_name", "investment_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO city.community_policing (customer_country, white) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "city.community_policing", "columns": ["customer_country", "white"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM oceanography\", conn)\ndf.to_sql(\"city_tech\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "oceanography", "columns": null}], "writes": [{"table": "city_tech", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO biodiversity SELECT * FROM legacy\ncur.execute(\"SELECT has_access, community_center_id FROM safety_research LIMIT 60\")\n", "labels": {"reads": [{"table": "safety_research", "columns": ["has_access", "community_center_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model astronauts depends on public_schools\ndbt build -s astronauts --vars '{\"src\":\"public_schools\"}'\n", "labels": {"reads": [{"table": "public_schools", "columns": null}], "writes": [{"table": "astronauts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model program_funding_2 depends on mart.inventory_hourly\ndbt run --models program_funding_2 --vars '{\"source_table\":\"mart.inventory_hourly\"}'\n", "labels": {"reads": [{"table": "mart.inventory_hourly", "columns": null}], "writes": [{"table": "program_funding_2", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO apartment_buildings SELECT amount_funded, case_type, community_id FROM restaurant WHERE amount_funded > 427\");\n", "labels": {"reads": [{"table": "restaurant", "columns": ["amount_funded", "case_type", "community_id"]}], "writes": [{"table": "apartment_buildings", "columns": ["amount_funded", "case_type", "community_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"suburbs\");\ndf.write().mode(\"overwrite\").saveAsTable(\"properties\");\n", "labels": {"reads": [{"table": "suburbs", "columns": null}], "writes": [{"table": "properties", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO bi.payments_daily SELECT material, investor_details, degrees, device_name FROM artist_concerts WHERE material > 96\")\n", "labels": {"reads": [{"table": "artist_concerts", "columns": ["material", "investor_details", "degrees", "device_name"]}], "writes": [{"table": "bi.payments_daily", "columns": ["material", "investor_details", "degrees", "device_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO miningoperations SELECT sample_date, max_age FROM stg.stg_risk_score_hourly WHERE sample_date > 329\"\n", "labels": {"reads": [{"table": "stg.stg_risk_score_hourly", "columns": ["sample_date", "max_age"]}], "writes": [{"table": "miningoperations", "columns": ["sample_date", "max_age"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO procedures SELECT * FROM legacy\nspark.sql(\"INSERT INTO round SELECT driver_id, rate, nominee FROM savings_programs WHERE driver_id > 453\")\n", "labels": {"reads": [{"table": "savings_programs", "columns": ["driver_id", "rate", "nominee"]}], "writes": [{"table": "round", "columns": ["driver_id", "rate", "nominee"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"donorprograms\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "donorprograms", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd.coupon_use_daily --columns round_type,offender_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd.coupon_use_daily", "columns": ["round_type", "offender_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO parties SELECT capacity_mw, unsure_rate, equipment FROM tourism_activities WHERE capacity_mw > 153\"\n", "labels": {"reads": [{"table": "tourism_activities", "columns": ["capacity_mw", "unsure_rate", "equipment"]}], "writes": [{"table": "parties", "columns": ["capacity_mw", "unsure_rate", "equipment"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM consumer_preference\"\n", "labels": {"reads": [{"table": "consumer_preference", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"spacecraft\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "spacecraft", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM station_crime_rates\", conn)\ndf.to_sql(\"user\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "station_crime_rates", "columns": null}], "writes": [{"table": "user", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model news_views depends on vessel\ndbt build --select news_views --vars '{\"src\":\"vessel\"}'\n", "labels": {"reads": [{"table": "vessel", "columns": null}], "writes": [{"table": "news_views", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"dispensary_sales\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"ods.ods_coupon_use_delta\")\n", "labels": {"reads": [{"table": "dispensary_sales", "columns": null}], "writes": [{"table": "ods.ods_coupon_use_delta", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO document_sections_images SELECT judge_state, veteran_id FROM inclusivehousingpolicies WHERE judge_state > 320\"\n", "labels": {"reads": [{"table": "inclusivehousingpolicies", "columns": ["judge_state", "veteran_id"]}], "writes": [{"table": "document_sections_images", "columns": ["judge_state", "veteran_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO staff_roles SELECT time_year, inspection_time, mhw_id, isfirstattendee FROM urban_initiatives WHERE time_year > 198\"\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": ["time_year", "inspection_time", "mhw_id", "isfirstattendee"]}], "writes": [{"table": "staff_roles", "columns": ["time_year", "inspection_time", "mhw_id", "isfirstattendee"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO dw.dw_orders_hourly SELECT injury_count, founding_date FROM geothermal_power_plants WHERE injury_count > 427\");\n", "labels": {"reads": [{"table": "geothermal_power_plants", "columns": ["injury_count", "founding_date"]}], "writes": [{"table": "dw.dw_orders_hourly", "columns": ["injury_count", "founding_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"military_equipment_maintenance\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"climate_communication_projects\")\n", "labels": {"reads": [{"table": "military_equipment_maintenance", "columns": null}], "writes": [{"table": "climate_communication_projects", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT funding_round_id, stateid FROM animal_population_status LIMIT 88\")\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO disabilityadvocacy SELECT restypename, member_in_charge_id, energy_efficiency_rating FROM ods.ods_campaigns_hourly WHERE restypename > 187\")\n", "labels": {"reads": [{"table": "animal_population_status", "columns": ["funding_round_id", "stateid"]}, {"table": "ods.ods_campaigns_hourly", "columns": ["restypename", "member_in_charge_id", "energy_efficiency_rating"]}], "writes": [{"table": "disabilityadvocacy", "columns": ["restypename", "member_in_charge_id", "energy_efficiency_rating"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO spacex_missions SELECT * FROM legacy\nspark.sql(\"INSERT INTO artist SELECT union_id, sqft, personnelbranch, sentence_id FROM program_funding_2 WHERE union_id > 31\")\n", "labels": {"reads": [{"table": "program_funding_2", "columns": ["union_id", "sqft", "personnelbranch", "sentence_id"]}], "writes": [{"table": "artist", "columns": ["union_id", "sqft", "personnelbranch", "sentence_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO talent_acquisition SELECT contributions, is_unionized, booked_count, founded FROM customer_size_diversity WHERE contributions > 466\")\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": ["contributions", "is_unionized", "booked_count", "founded"]}], "writes": [{"table": "talent_acquisition", "columns": ["contributions", "is_unionized", "booked_count", "founded"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO productsafety SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO therapy_attendance SELECT * FROM legacy\nspark.sql(\"INSERT INTO ref_locations SELECT document_type_description, headquarters FROM ads.ads_events_df WHERE document_type_description > 371\")\n", "labels": {"reads": [{"table": "ads.ads_events_df", "columns": ["document_type_description", "headquarters"]}], "writes": [{"table": "ref_locations", "columns": ["document_type_description", "headquarters"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO water_usage SELECT consumption, disability_type FROM farmers WHERE consumption > 209\"\n", "labels": {"reads": [{"table": "farmers", "columns": ["consumption", "disability_type"]}], "writes": [{"table": "water_usage", "columns": ["consumption", "disability_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"voyages\");\ndf.write().mode(\"overwrite\").saveAsTable(\"government.region\");\n", "labels": {"reads": [{"table": "voyages", "columns": null}], "writes": [{"table": "government.region", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT region_name, official_native_language FROM player_sessions\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nif not rows:\n logger.warning('empty result')\ndf.to_sql(\"university\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "player_sessions", "columns": ["region_name", "official_native_language"]}], "writes": [{"table": "university", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"user_likes\");\ndf.write().mode(\"overwrite\").saveAsTable(\"rare_earth_companies\");\n", "labels": {"reads": [{"table": "user_likes", "columns": null}], "writes": [{"table": "rare_earth_companies", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"attribute_definitions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "attribute_definitions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table hair_care_sales --target-dir /tmp/land\n", "labels": {"reads": [{"table": "hair_care_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO supplier_ethics SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"stations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"categories\")\n", "labels": {"reads": [{"table": "stations", "columns": null}], "writes": [{"table": "categories", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"electoral_register\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"marketingbudget\")\n", "labels": {"reads": [{"table": "electoral_register", "columns": null}], "writes": [{"table": "marketingbudget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ads.users_full\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ads.users_full", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO culturalcompetencytrainings SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.bedtype > 291).all()\n# src table: safety_violations\nengine.execute(\"INSERT INTO paris_real_estate SELECT * FROM safety_violations\")\n", "labels": {"reads": [{"table": "safety_violations", "columns": null}], "writes": [{"table": "paris_real_estate", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"communityengagement\")\nsrc.write.insertInto(\"human_resources\", overwrite=True)\n", "labels": {"reads": [{"table": "communityengagement", "columns": null}], "writes": [{"table": "human_resources", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO financial_capability SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO savings_programs SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT longitude, shipping_agent_name FROM electoral_register LIMIT 296\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "electoral_register", "columns": ["longitude", "shipping_agent_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.wellname > 22).all()\n# src table: disaster_response_donations\nengine.execute(\"INSERT INTO features SELECT * FROM disaster_response_donations\")\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": null}], "writes": [{"table": "features", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"program_history\");\ndf.write().mode(\"overwrite\").saveAsTable(\"athletes\");\n", "labels": {"reads": [{"table": "program_history", "columns": null}], "writes": [{"table": "athletes", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model vulnerabilities depends on mart_orders_di\ndbt build --models vulnerabilities --vars '{\"src\":\"mart_orders_di\"}'\n", "labels": {"reads": [{"table": "mart_orders_di", "columns": null}], "writes": [{"table": "vulnerabilities", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO maintenancerequests SELECT is_public, court_id, storeid FROM factory_connections WHERE is_public > 188\");\n", "labels": {"reads": [{"table": "factory_connections", "columns": ["is_public", "court_id", "storeid"]}], "writes": [{"table": "maintenancerequests", "columns": ["is_public", "court_id", "storeid"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO dw.dw_sessions_delta SELECT * FROM legacy\nspark.sql(\"INSERT INTO clinics_sa SELECT left_office, games FROM assets WHERE left_office > 331\")\n", "labels": {"reads": [{"table": "assets", "columns": ["left_office", "games"]}], "writes": [{"table": "clinics_sa", "columns": ["left_office", "games"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.gameid > 383).all()\n# src table: elimination\nengine.execute(\"INSERT INTO plays_games SELECT * FROM elimination\")\n", "labels": {"reads": [{"table": "elimination", "columns": null}], "writes": [{"table": "plays_games", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_building\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"topublictransportation\")\n", "labels": {"reads": [{"table": "sustainable_building", "columns": null}], "writes": [{"table": "topublictransportation", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.ods_risk_score_delta\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"farmer_details\")\n", "labels": {"reads": [{"table": "ods.ods_risk_score_delta", "columns": null}], "writes": [{"table": "farmer_details", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO bi.risk_score_df SELECT startyear, stu_dob FROM driver WHERE startyear > 377\")\n", "labels": {"reads": [{"table": "driver", "columns": ["startyear", "stu_dob"]}], "writes": [{"table": "bi.risk_score_df", "columns": ["startyear", "stu_dob"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"party_forms\");\ndf.write().mode(\"overwrite\").saveAsTable(\"field\");\n", "labels": {"reads": [{"table": "party_forms", "columns": null}], "writes": [{"table": "field", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"player_f\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bank\")\n", "labels": {"reads": [{"table": "player_f", "columns": null}], "writes": [{"table": "bank", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT subject_id, driverid FROM public_transportation_sydney LIMIT 72\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": ["subject_id", "driverid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"digital_assets\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "digital_assets", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO mart.mart_payments_hourly SELECT phone_id, donation_date, threat_type, security_level FROM temperaturerecords WHERE phone_id > 352\")\n", "labels": {"reads": [{"table": "temperaturerecords", "columns": ["phone_id", "donation_date", "threat_type", "security_level"]}], "writes": [{"table": "mart.mart_payments_hourly", "columns": ["phone_id", "donation_date", "threat_type", "security_level"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO traffic_violations (last_year, fund_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "traffic_violations", "columns": ["last_year", "fund_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO swimmer SELECT train_number, publication_id FROM donors_region WHERE train_number > 353\")\n", "labels": {"reads": [{"table": "donors_region", "columns": ["train_number", "publication_id"]}], "writes": [{"table": "swimmer", "columns": ["train_number", "publication_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"justice_schemas.legal_tech_providers\").toPandas()\ndf[[\"route_id\", \"min_salary\"]].to_sql(\"enrolled_in\", engine, index=False)\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": null}], "writes": [{"table": "enrolled_in", "columns": ["route_id", "min_salary"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"communitycourts\")\nsrc.write.insertInto(\"salary\", overwrite=True)\n", "labels": {"reads": [{"table": "communitycourts", "columns": null}], "writes": [{"table": "salary", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO financialwellbeing (shares, driver_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "financialwellbeing", "columns": ["shares", "driver_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO water_sources SELECT job_title, medical_risk FROM satellitedata WHERE job_title > 131\");\n", "labels": {"reads": [{"table": "satellitedata", "columns": ["job_title", "medical_risk"]}], "writes": [{"table": "water_sources", "columns": ["job_title", "medical_risk"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT system_name, mgr_start_date FROM road_construction LIMIT 384\")\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO sustainability SELECT truck_licence_number, id, inspection_id, product_quantity FROM portfolios WHERE truck_licence_number > 419\")\n", "labels": {"reads": [{"table": "road_construction", "columns": ["system_name", "mgr_start_date"]}, {"table": "portfolios", "columns": ["truck_licence_number", "id", "inspection_id", "product_quantity"]}], "writes": [{"table": "sustainability", "columns": ["truck_licence_number", "id", "inspection_id", "product_quantity"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ods.ods_coupon_use_di SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT ocean_name, refugee_name FROM community.donors LIMIT 100\")\nif not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO school_bus SELECT start_date, budgetid, apt_number FROM ads.ads_device_log_di WHERE start_date > 87\")\n", "labels": {"reads": [{"table": "community.donors", "columns": ["ocean_name", "refugee_name"]}, {"table": "ads.ads_device_log_di", "columns": ["start_date", "budgetid", "apt_number"]}], "writes": [{"table": "school_bus", "columns": ["start_date", "budgetid", "apt_number"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO militarycyberops SELECT stu_dob, pettype FROM on_call WHERE stu_dob > 186\"\n", "labels": {"reads": [{"table": "on_call", "columns": ["stu_dob", "pettype"]}], "writes": [{"table": "militarycyberops", "columns": ["stu_dob", "pettype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 83;\nEOF\n", "labels": {"reads": [{"table": "contract_states", "columns": ["donorage", "response_received_date"]}], "writes": [{"table": "manufacturer", "columns": ["donorage", "response_received_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"esportsteamsafrica\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "esportsteamsafrica", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.circuitid > 102).all()\n# src table: drama_workshop_groups\nengine.execute(\"INSERT INTO workshops SELECT * FROM drama_workshop_groups\")\n", "labels": {"reads": [{"table": "drama_workshop_groups", "columns": null}], "writes": [{"table": "workshops", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO volume SELECT a.costid, b.acidity FROM impact_investments a JOIN circularsupplychain b ON a.explainability_score = b.explainability_score\"\n", "labels": {"reads": [{"table": "impact_investments", "columns": null}, {"table": "circularsupplychain", "columns": null}], "writes": [{"table": "volume", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO ref_locations SELECT * FROM legacy\nspark.sql(\"INSERT INTO workercontactinfo SELECT catalog_level_name, train_id, co2_emission FROM concert_sales WHERE catalog_level_name > 387\")\n", "labels": {"reads": [{"table": "concert_sales", "columns": ["catalog_level_name", "train_id", "co2_emission"]}], "writes": [{"table": "workercontactinfo", "columns": ["catalog_level_name", "train_id", "co2_emission"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO mart.mart_payments_delta SELECT a.stream_date, b.project_name FROM recyclingrates a JOIN london.stations b ON a.half = b.half\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "recyclingrates", "columns": null}, {"table": "london.stations", "columns": null}], "writes": [{"table": "mart.mart_payments_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO coralreefs SELECT operating_system, job_category, workout_id FROM authors WHERE operating_system > 252\"], check=True)\n", "labels": {"reads": [{"table": "authors", "columns": ["operating_system", "job_category", "workout_id"]}], "writes": [{"table": "coralreefs", "columns": ["operating_system", "job_category", "workout_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"tryout\").toPandas()\ndf[[\"volunteer_id\", \"stateid\"]].to_sql(\"water_conservation_brazil\", engine, index=False)\n", "labels": {"reads": [{"table": "tryout", "columns": null}], "writes": [{"table": "water_conservation_brazil", "columns": ["volunteer_id", "stateid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO reviews SELECT detected_at, court_id FROM africa_schema.african_mines WHERE detected_at > 231\");\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["detected_at", "court_id"]}], "writes": [{"table": "reviews", "columns": ["detected_at", "court_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO militarycyberops SELECT medicine_id, trip_id, billing_city, loadingend FROM donations2022 WHERE medicine_id > 29\");\n", "labels": {"reads": [{"table": "donations2022", "columns": ["medicine_id", "trip_id", "billing_city", "loadingend"]}], "writes": [{"table": "militarycyberops", "columns": ["medicine_id", "trip_id", "billing_city", "loadingend"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"pharmasales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"archaeologists\")\n", "labels": {"reads": [{"table": "pharmasales", "columns": null}], "writes": [{"table": "archaeologists", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"water_conservation\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"vehicles\")\n", "labels": {"reads": [{"table": "water_conservation", "columns": null}], "writes": [{"table": "vehicles", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT production_value, aircraft_id FROM ais LIMIT 165\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO community.donations SELECT gradepoint, menucategory, amount_outstanding FROM volunteer_registration WHERE gradepoint > 451\")\n", "labels": {"reads": [{"table": "ais", "columns": ["production_value", "aircraft_id"]}, {"table": "volunteer_registration", "columns": ["gradepoint", "menucategory", "amount_outstanding"]}], "writes": [{"table": "community.donations", "columns": ["gradepoint", "menucategory", "amount_outstanding"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO education_programs SELECT quantityproduced, donor_category, contractor_id FROM event_attendance WHERE quantityproduced > 280\");\n", "labels": {"reads": [{"table": "event_attendance", "columns": ["quantityproduced", "donor_category", "contractor_id"]}], "writes": [{"table": "education_programs", "columns": ["quantityproduced", "donor_category", "contractor_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"artsales\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "artsales", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO employee SELECT a.affirmative, b.census_ranking FROM circuits a JOIN donation b ON a.delivery_time = b.delivery_time\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "circuits", "columns": null}, {"table": "donation", "columns": null}], "writes": [{"table": "employee", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"nutritionfacts\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "nutritionfacts", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO crops_year SELECT * FROM legacy\nspark.sql(\"INSERT INTO concert_revenue SELECT offender_name, bdate FROM freightforwarding WHERE offender_name > 365\")\n", "labels": {"reads": [{"table": "freightforwarding", "columns": ["offender_name", "bdate"]}], "writes": [{"table": "concert_revenue", "columns": ["offender_name", "bdate"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT emission_date, dish_id FROM teacher_pd LIMIT 7\")\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO temperature SELECT treatment_date, gender_code FROM ads.inventory_di WHERE treatment_date > 421\")\n", "labels": {"reads": [{"table": "teacher_pd", "columns": ["emission_date", "dish_id"]}, {"table": "ads.inventory_di", "columns": ["treatment_date", "gender_code"]}], "writes": [{"table": "temperature", "columns": ["treatment_date", "gender_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"open_pedagogy_exam\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"city_waste_generation\")\n", "labels": {"reads": [{"table": "open_pedagogy_exam", "columns": null}], "writes": [{"table": "city_waste_generation", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO supply_chain SELECT * FROM legacy\ncur.execute(\"SELECT cvid, portfolio_id FROM al_jazeera_data LIMIT 459\")\n", "labels": {"reads": [{"table": "al_jazeera_data", "columns": ["cvid", "portfolio_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model tb_cases depends on military_equipment_maintenance\ndbt run -s tb_cases --vars 'source: military_equipment_maintenance'\n", "labels": {"reads": [{"table": "military_equipment_maintenance", "columns": null}], "writes": [{"table": "tb_cases", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table concert_sales --target-dir /tmp/land\n", "labels": {"reads": [{"table": "concert_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dw.dw_events_di SELECT assessmentid, scientist, transaction_category, license_number FROM asia_events WHERE assessmentid > 109\"], check=True)\n", "labels": {"reads": [{"table": "asia_events", "columns": ["assessmentid", "scientist", "transaction_category", "license_number"]}], "writes": [{"table": "dw.dw_events_di", "columns": ["assessmentid", "scientist", "transaction_category", "license_number"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO materials_usage SELECT chemical_id, population, workoutid FROM third_party_companies WHERE chemical_id > 411\")\n", "labels": {"reads": [{"table": "third_party_companies", "columns": ["chemical_id", "population", "workoutid"]}], "writes": [{"table": "materials_usage", "columns": ["chemical_id", "population", "workoutid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO districts SELECT completion_year, energy_efficiency_kwh_m2_year FROM all_star WHERE completion_year > 389\");\n", "labels": {"reads": [{"table": "all_star", "columns": ["completion_year", "energy_efficiency_kwh_m2_year"]}], "writes": [{"table": "districts", "columns": ["completion_year", "energy_efficiency_kwh_m2_year"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table sales_2 --columns host_city_id,sector_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "sales_2", "columns": ["host_city_id", "sector_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO education_programs SELECT product_color, permitdate FROM lives_in WHERE product_color > 218\");\n", "labels": {"reads": [{"table": "lives_in", "columns": ["product_color", "permitdate"]}], "writes": [{"table": "education_programs", "columns": ["product_color", "permitdate"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO customer_transactions SELECT workshop_name, organization_id FROM astronaut_medical_3 WHERE workshop_name > 165\")\n", "labels": {"reads": [{"table": "astronaut_medical_3", "columns": ["workshop_name", "organization_id"]}], "writes": [{"table": "customer_transactions", "columns": ["workshop_name", "organization_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO languages SELECT fairness_score, fan_name, model_id, response_type FROM immunization WHERE fairness_score > 174\"\n", "labels": {"reads": [{"table": "immunization", "columns": ["fairness_score", "fan_name", "model_id", "response_type"]}], "writes": [{"table": "languages", "columns": ["fairness_score", "fan_name", "model_id", "response_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO communities SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"levees\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"decentralized_applications\")\n", "labels": {"reads": [{"table": "levees", "columns": null}], "writes": [{"table": "decentralized_applications", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw.users_hourly\")\nsrc.write.insertInto(\"virtual_tourism\", overwrite=True)\n", "labels": {"reads": [{"table": "dw.users_hourly", "columns": null}], "writes": [{"table": "virtual_tourism", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT flno, catalog_name FROM dws.dws_events_hourly LIMIT 420\")\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO country_waste_generation SELECT shipment_id, campaign_id, playtime, workers FROM ads.orders_daily WHERE shipment_id > 13\")\n", "labels": {"reads": [{"table": "dws.dws_events_hourly", "columns": ["flno", "catalog_name"]}, {"table": "ads.orders_daily", "columns": ["shipment_id", "campaign_id", "playtime", "workers"]}], "writes": [{"table": "country_waste_generation", "columns": ["shipment_id", "campaign_id", "playtime", "workers"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table criminal_justice_reform_initiatives --target-dir /tmp/land\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"traditionalarts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"takes\")\n", "labels": {"reads": [{"table": "traditionalarts", "columns": null}], "writes": [{"table": "takes", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO renewabletypes SELECT portname, document_structure_description FROM aid_missions WHERE portname > 475\"\n", "labels": {"reads": [{"table": "aid_missions", "columns": ["portname", "document_structure_description"]}], "writes": [{"table": "renewabletypes", "columns": ["portname", "document_structure_description"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hourlyrate, structure_type FROM authors\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"mine_workforce\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "authors", "columns": ["hourlyrate", "structure_type"]}], "writes": [{"table": "mine_workforce", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO acceptance SELECT operating_system, date_problem_reported, painting_name FROM inclusive_housing WHERE operating_system > 296\"\n", "labels": {"reads": [{"table": "inclusive_housing", "columns": ["operating_system", "date_problem_reported", "painting_name"]}], "writes": [{"table": "acceptance", "columns": ["operating_system", "date_problem_reported", "painting_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO infra_diversification SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 176;\nEOF\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": ["publicationid", "launch_date"]}], "writes": [{"table": "stg.stg_coupon_use_hourly", "columns": ["publicationid", "launch_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO space_programs SELECT kids, community_members FROM miningwaterusage WHERE kids > 19\")\n", "labels": {"reads": [{"table": "miningwaterusage", "columns": ["kids", "community_members"]}], "writes": [{"table": "space_programs", "columns": ["kids", "community_members"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"infantmortalitydata\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT browser_id, circuitid FROM sectors LIMIT 343\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO esportsevents SELECT paper_id, ethical_manufacturing FROM university WHERE paper_id > 44\")\n", "labels": {"reads": [{"table": "sectors", "columns": ["browser_id", "circuitid"]}, {"table": "university", "columns": ["paper_id", "ethical_manufacturing"]}], "writes": [{"table": "esportsevents", "columns": ["paper_id", "ethical_manufacturing"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO bi.bi_campaigns_delta SELECT volunteerage, provider_parity_score, participant_type_code FROM electric_vehicle_stats WHERE volunteerage > 98\"\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": ["volunteerage", "provider_parity_score", "participant_type_code"]}], "writes": [{"table": "bi.bi_campaigns_delta", "columns": ["volunteerage", "provider_parity_score", "participant_type_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM regional_archaeologists\", conn)\ndf.to_sql(\"projectemployees\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "regional_archaeologists", "columns": null}], "writes": [{"table": "projectemployees", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model head depends on customer_addresses\ndbt run --models head --vars 'source: customer_addresses'\n", "labels": {"reads": [{"table": "customer_addresses", "columns": null}], "writes": [{"table": "head", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_inventory_full\"\n", "labels": {"reads": [{"table": "bi.bi_inventory_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO imagery_archive (mealid, product_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "imagery_archive", "columns": ["mealid", "product_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nspark.sql(\"INSERT INTO players SELECT visit_month, enr FROM public.developers WHERE visit_month > 185\")\n", "labels": {"reads": [{"table": "public.developers", "columns": ["visit_month", "enr"]}], "writes": [{"table": "players", "columns": ["visit_month", "enr"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"farmers\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"workout_data\")\n", "labels": {"reads": [{"table": "farmers", "columns": null}], "writes": [{"table": "workout_data", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO overwatch_scores SELECT stationid, pilot, last_checkup_date, cost_id FROM tech_for_social_good WHERE stationid > 39\"\n", "labels": {"reads": [{"table": "tech_for_social_good", "columns": ["stationid", "pilot", "last_checkup_date", "cost_id"]}], "writes": [{"table": "overwatch_scores", "columns": ["stationid", "pilot", "last_checkup_date", "cost_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO track SELECT * FROM legacy\ncur.execute(\"SELECT catalog_level_number, course_name FROM tourdifferences LIMIT 171\")\n", "labels": {"reads": [{"table": "tourdifferences", "columns": ["catalog_level_number", "course_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO classicgame SELECT a.jan, b.incident_category FROM weekly_weather a JOIN workforce_training b ON a.comments = b.comments\"\n", "labels": {"reads": [{"table": "weekly_weather", "columns": null}, {"table": "workforce_training", "columns": null}], "writes": [{"table": "classicgame", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"tryout\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"intelligence_agency\")\n", "labels": {"reads": [{"table": "tryout", "columns": null}], "writes": [{"table": "intelligence_agency", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.goal_id > 54).all()\n# src table: classroom\nengine.execute(\"INSERT INTO mart.shipments_df SELECT * FROM classroom\")\n", "labels": {"reads": [{"table": "classroom", "columns": null}], "writes": [{"table": "mart.shipments_df", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"e_scooter_trips\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO academic_publications SELECT document_structure_description, domestic_passengers, precedent_id, role_code FROM categories WHERE document_structure_description > 161\");\n", "labels": {"reads": [{"table": "categories", "columns": ["document_structure_description", "domestic_passengers", "precedent_id", "role_code"]}], "writes": [{"table": "academic_publications", "columns": ["document_structure_description", "domestic_passengers", "precedent_id", "role_code"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO concert SELECT a.center_id, b.strain FROM timbersales a JOIN grant b ON a.donationamount = b.donationamount\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "timbersales", "columns": null}, {"table": "grant", "columns": null}], "writes": [{"table": "concert", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO housingaffordability SELECT emp_lname, access_date, characteristic_type_code FROM soilanalysis WHERE emp_lname > 196\")\n", "labels": {"reads": [{"table": "soilanalysis", "columns": ["emp_lname", "access_date", "characteristic_type_code"]}], "writes": [{"table": "housingaffordability", "columns": ["emp_lname", "access_date", "characteristic_type_code"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model ngo_funding depends on energy_efficiency_projects\ndbt build --select ngo_funding --vars 'source: energy_efficiency_projects'\n", "labels": {"reads": [{"table": "energy_efficiency_projects", "columns": null}], "writes": [{"table": "ngo_funding", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"smartcitytech\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "smartcitytech", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM development_hours\", conn)\ndf.to_sql(\"airport\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "development_hours", "columns": null}], "writes": [{"table": "airport", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO seamounts (green_building_certified, eco_certified) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "seamounts", "columns": ["green_building_certified", "eco_certified"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO cargo_tracking SELECT workout_date, num_attendees, issues, date_of_completion FROM mentalhealthscores WHERE workout_date > 395\");\n", "labels": {"reads": [{"table": "mentalhealthscores", "columns": ["workout_date", "num_attendees", "issues", "date_of_completion"]}], "writes": [{"table": "cargo_tracking", "columns": ["workout_date", "num_attendees", "issues", "date_of_completion"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT famous_title, missing_data FROM manufacturer LIMIT 121\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "manufacturer", "columns": ["famous_title", "missing_data"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\nspark.sql(\"INSERT INTO mine_workforce SELECT provider, site_name FROM landfills WHERE provider > 174\")\n", "labels": {"reads": [{"table": "landfills", "columns": ["provider", "site_name"]}], "writes": [{"table": "mine_workforce", "columns": ["provider", "site_name"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"feed\");\ndf.write().mode(\"overwrite\").saveAsTable(\"budgets\");\n", "labels": {"reads": [{"table": "feed", "columns": null}], "writes": [{"table": "budgets", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM news_report\"\n", "labels": {"reads": [{"table": "news_report", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ref_colors\", conn)\ndf.to_sql(\"birds\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ref_colors", "columns": null}], "writes": [{"table": "birds", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 106;\nEOF\n", "labels": {"reads": [{"table": "perpetrator", "columns": ["is_eco_friendly", "prepnurse", "restypename", "flight_number"]}], "writes": [{"table": "shipment_data", "columns": ["is_eco_friendly", "prepnurse", "restypename", "flight_number"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"pacific_ocean\").toPandas()\ndf[[\"don_id\", \"astronaut_name\"]].to_sql(\"defense_contracts_v2\", engine, index=False)\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": null}], "writes": [{"table": "defense_contracts_v2", "columns": ["don_id", "astronaut_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO job_postings SELECT min_salary, investmenttype FROM mart.coupon_use_hourly WHERE min_salary > 16\"], check=True)\n", "labels": {"reads": [{"table": "mart.coupon_use_hourly", "columns": ["min_salary", "investmenttype"]}], "writes": [{"table": "job_postings", "columns": ["min_salary", "investmenttype"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"music_festival\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "music_festival", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT retail_price, curator FROM public_transportation_sydney LIMIT 405\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO certifications SELECT group_name, organic_ingredients_percentage, graduate FROM auto_show WHERE group_name > 152\")\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": ["retail_price", "curator"]}, {"table": "auto_show", "columns": ["group_name", "organic_ingredients_percentage", "graduate"]}], "writes": [{"table": "certifications", "columns": ["group_name", "organic_ingredients_percentage", "graduate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM attribute_definitions\"\n", "labels": {"reads": [{"table": "attribute_definitions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.thefttypeid > 415).all()\n# src table: economic_diversification\nengine.execute(\"INSERT INTO mappinglengths SELECT * FROM economic_diversification\")\n", "labels": {"reads": [{"table": "economic_diversification", "columns": null}], "writes": [{"table": "mappinglengths", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT floor_area_m2, fund_id FROM tourist_attractions\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"providers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "tourist_attractions", "columns": ["floor_area_m2", "fund_id"]}], "writes": [{"table": "providers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.team_id_winner > 152).all()\n# src table: researchgrants\nengine.execute(\"INSERT INTO tech_workers_union SELECT * FROM researchgrants\")\n", "labels": {"reads": [{"table": "researchgrants", "columns": null}], "writes": [{"table": "tech_workers_union", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"sustainable_practices\");\ndf.write().mode(\"overwrite\").saveAsTable(\"bus_fares\");\n", "labels": {"reads": [{"table": "sustainable_practices", "columns": null}], "writes": [{"table": "bus_fares", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO animal_species SELECT matchdate, quantitysold, project FROM sitem WHERE matchdate > 175\"\n", "labels": {"reads": [{"table": "sitem", "columns": ["matchdate", "quantitysold", "project"]}], "writes": [{"table": "animal_species", "columns": ["matchdate", "quantitysold", "project"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO visual_arts SELECT publisher, condition_id, movieid, blockfloor FROM research_vessels WHERE publisher > 209\"\n", "labels": {"reads": [{"table": "research_vessels", "columns": ["publisher", "condition_id", "movieid", "blockfloor"]}], "writes": [{"table": "visual_arts", "columns": ["publisher", "condition_id", "movieid", "blockfloor"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mart.mart_coupon_use_full\");\ndf.write().mode(\"overwrite\").saveAsTable(\"sustainable_practices\");\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_full", "columns": null}], "writes": [{"table": "sustainable_practices", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"government.city\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "government.city", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model innovation_grants depends on geologicalsurvey\ndbt build -s innovation_grants --vars '{\"src\":\"geologicalsurvey\"}'\n", "labels": {"reads": [{"table": "geologicalsurvey", "columns": null}], "writes": [{"table": "innovation_grants", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"emergency_calls\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "emergency_calls", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 186;\nEOF\n", "labels": {"reads": [{"table": "communities", "columns": ["document_structure_description", "target_u_id"]}], "writes": [{"table": "institution", "columns": ["document_structure_description", "target_u_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO broadband_subscribers (sector, mineid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "broadband_subscribers", "columns": ["sector", "mineid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT workoutdate, trench_id FROM tree_species\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"fairtradecertification\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "tree_species", "columns": ["workoutdate", "trench_id"]}], "writes": [{"table": "fairtradecertification", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table articles --target-dir /tmp/land\n", "labels": {"reads": [{"table": "articles", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO mailshot_customers SELECT singer_id, propertyid, reporter_id, county_id FROM fans_merchandise_basketball WHERE singer_id > 113\");\n", "labels": {"reads": [{"table": "fans_merchandise_basketball", "columns": ["singer_id", "propertyid", "reporter_id", "county_id"]}], "writes": [{"table": "mailshot_customers", "columns": ["singer_id", "propertyid", "reporter_id", "county_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"department_store_chain\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "department_store_chain", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"dwd_sessions_hourly\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "dwd_sessions_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"assignedto\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "assignedto", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"ods.ods_users_di\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"environmentalimpact\")\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": null}], "writes": [{"table": "environmentalimpact", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO public.police_calls SELECT a.founder_lgbtq, b.ota_name FROM decentralized_applications a JOIN precipitation_data b ON a.type = b.type\"\n", "labels": {"reads": [{"table": "decentralized_applications", "columns": null}, {"table": "precipitation_data", "columns": null}], "writes": [{"table": "public.police_calls", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"film_actor\");\ndf.write().mode(\"overwrite\").saveAsTable(\"systems\");\n", "labels": {"reads": [{"table": "film_actor", "columns": null}], "writes": [{"table": "systems", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"program_funding_2\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "program_funding_2", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO visualartprograms SELECT assigned_to_staff_id, ocean_name FROM student_course_attendance WHERE assigned_to_staff_id > 303\");\n", "labels": {"reads": [{"table": "student_course_attendance", "columns": ["assigned_to_staff_id", "ocean_name"]}], "writes": [{"table": "visualartprograms", "columns": ["assigned_to_staff_id", "ocean_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO shared_ebikes SELECT * FROM legacy\nspark.sql(\"INSERT INTO communityhealthworkers SELECT dependent_name, case_id, mineid, store_name FROM innovation_projects WHERE dependent_name > 236\")\n", "labels": {"reads": [{"table": "innovation_projects", "columns": ["dependent_name", "case_id", "mineid", "store_name"]}], "writes": [{"table": "communityhealthworkers", "columns": ["dependent_name", "case_id", "mineid", "store_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO arcticocean SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO charging_stations SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"parties\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"aus_wellbeing\")\n", "labels": {"reads": [{"table": "parties", "columns": null}], "writes": [{"table": "aus_wellbeing", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO product_reviews SELECT * FROM legacy\nspark.sql(\"INSERT INTO attendee_demographics SELECT gas_production_2020, long FROM ytterbiumproduction WHERE gas_production_2020 > 293\")\n", "labels": {"reads": [{"table": "ytterbiumproduction", "columns": ["gas_production_2020", "long"]}], "writes": [{"table": "attendee_demographics", "columns": ["gas_production_2020", "long"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM autonomousvehicles\", conn)\ndf.to_sql(\"ods.ods_events_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "autonomousvehicles", "columns": null}], "writes": [{"table": "ods.ods_events_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO product_review SELECT crs_code, ai_adoption, year_founded, production_date FROM therapy WHERE crs_code > 42\")\n", "labels": {"reads": [{"table": "therapy", "columns": ["crs_code", "ai_adoption", "year_founded", "production_date"]}], "writes": [{"table": "product_review", "columns": ["crs_code", "ai_adoption", "year_founded", "production_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO total_consumption SELECT a.petid, b.hire_date FROM urban_transportation a JOIN satellites_in_orbit b ON a.origin = b.origin\"\n", "labels": {"reads": [{"table": "urban_transportation", "columns": null}, {"table": "satellites_in_orbit", "columns": null}], "writes": [{"table": "total_consumption", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"biosensors.projects\")\ndump_to_sink(df, \"florida_conservation_initiatives\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": null}], "writes": [{"table": "florida_conservation_initiatives", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO us_military_personnel (therapy_session, total_shipped) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "us_military_personnel", "columns": ["therapy_session", "total_shipped"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"spacecraft_temperatures\").where(\"dt = current_date()\").writeTo(\"workshops\").append()\n", "labels": {"reads": [{"table": "spacecraft_temperatures", "columns": null}], "writes": [{"table": "workshops", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT wheels, electoral_register_id FROM educators LIMIT 34\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO prescribes SELECT hotel_chain_name, salesperson_id, request_id, experience FROM train_lines WHERE hotel_chain_name > 477\")\n", "labels": {"reads": [{"table": "educators", "columns": ["wheels", "electoral_register_id"]}, {"table": "train_lines", "columns": ["hotel_chain_name", "salesperson_id", "request_id", "experience"]}], "writes": [{"table": "prescribes", "columns": ["hotel_chain_name", "salesperson_id", "request_id", "experience"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\nspark.sql(\"INSERT INTO stg.stg_users SELECT class_room, gradepoint FROM pilot WHERE class_room > 17\")\n", "labels": {"reads": [{"table": "pilot", "columns": ["class_room", "gradepoint"]}], "writes": [{"table": "stg.stg_users", "columns": ["class_room", "gradepoint"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO nutrition_facts SELECT building_address, source_system_code, taskid, workoutname FROM stg.campaigns_daily WHERE building_address > 82\")\n", "labels": {"reads": [{"table": "stg.campaigns_daily", "columns": ["building_address", "source_system_code", "taskid", "workoutname"]}], "writes": [{"table": "nutrition_facts", "columns": ["building_address", "source_system_code", "taskid", "workoutname"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO ads.risk_score SELECT max_cargo_weight, station_name, contributor FROM uel_top10 WHERE max_cargo_weight > 329\");\n", "labels": {"reads": [{"table": "uel_top10", "columns": ["max_cargo_weight", "station_name", "contributor"]}], "writes": [{"table": "ads.risk_score", "columns": ["max_cargo_weight", "station_name", "contributor"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"item_inventory\").where(\"dt = current_date()\").writeTo(\"ads.ads_exposure_di\").append()\n", "labels": {"reads": [{"table": "item_inventory", "columns": null}], "writes": [{"table": "ads.ads_exposure_di", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"climate_data\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"sustainable_menu_items\")\n", "labels": {"reads": [{"table": "climate_data", "columns": null}], "writes": [{"table": "sustainable_menu_items", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT detention_summary, diversity_score FROM freightforwarding LIMIT 295\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [{"table": "freightforwarding", "columns": ["detention_summary", "diversity_score"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.service_id > 5).all()\n# src table: ads.ads_users_hourly\nengine.execute(\"INSERT INTO ref_budget_codes SELECT * FROM ads.ads_users_hourly\")\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": null}], "writes": [{"table": "ref_budget_codes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO socialimpactinvestments (habitat_name, date_incident_start) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "socialimpactinvestments", "columns": ["habitat_name", "date_incident_start"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO circuits SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"skincare_sales\").toPandas()\ndf[[\"injury\", \"startyear\"]].to_sql(\"fare_collection\", engine, index=False)\n", "labels": {"reads": [{"table": "skincare_sales", "columns": null}], "writes": [{"table": "fare_collection", "columns": ["injury", "startyear"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO community_health_workers SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"airport\")\nsrc.write.insertInto(\"graduates\", overwrite=True)\n", "labels": {"reads": [{"table": "airport", "columns": null}], "writes": [{"table": "graduates", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO editor SELECT galleryname, response_received_date, crs_description, negative FROM ref_document_status WHERE galleryname > 447\"], check=True)\n", "labels": {"reads": [{"table": "ref_document_status", "columns": ["galleryname", "response_received_date", "crs_description", "negative"]}], "writes": [{"table": "editor", "columns": ["galleryname", "response_received_date", "crs_description", "negative"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"material_production\").where(\"dt = current_date()\").writeTo(\"ads.refunds\").append()\n", "labels": {"reads": [{"table": "material_production", "columns": null}], "writes": [{"table": "ads.refunds", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"astronaut_medical_3\").where(\"dt = current_date()\").writeTo(\"bi.bi_payments_delta\").append()\n", "labels": {"reads": [{"table": "astronaut_medical_3", "columns": null}], "writes": [{"table": "bi.bi_payments_delta", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibition_visits\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"marine_conservation\")\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": null}], "writes": [{"table": "marine_conservation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO body_builder SELECT price_per_gram, horizontal_bar_points FROM document_structures WHERE price_per_gram > 96\"], check=True)\n", "labels": {"reads": [{"table": "document_structures", "columns": ["price_per_gram", "horizontal_bar_points"]}], "writes": [{"table": "body_builder", "columns": ["price_per_gram", "horizontal_bar_points"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO ods.ods_exposure_delta SELECT avg_speed, max_depth, assistingnurse FROM concert_revenue WHERE avg_speed > 494\");\n", "labels": {"reads": [{"table": "concert_revenue", "columns": ["avg_speed", "max_depth", "assistingnurse"]}], "writes": [{"table": "ods.ods_exposure_delta", "columns": ["avg_speed", "max_depth", "assistingnurse"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"africa_schema.african_mines\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"product_review\")\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": null}], "writes": [{"table": "product_review", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO transportation_per_country SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO apartments SELECT home_team_three_point, tournament_name FROM public_transport.passenger_count WHERE home_team_three_point > 152\");\n", "labels": {"reads": [{"table": "public_transport.passenger_count", "columns": ["home_team_three_point", "tournament_name"]}], "writes": [{"table": "apartments", "columns": ["home_team_three_point", "tournament_name"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO taxi_data SELECT a.ironquantity, b.royal_family_id FROM seeds a JOIN artsandcrafts b ON a.weight = b.weight\"\n", "labels": {"reads": [{"table": "seeds", "columns": null}, {"table": "artsandcrafts", "columns": null}], "writes": [{"table": "taxi_data", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO eu_humanitarian_assistance SELECT a.attendanceid, b.safety_record FROM mineral_extraction a JOIN safety_incidents b ON a.away_team_three_point = b.away_team_three_point\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mineral_extraction", "columns": null}, {"table": "safety_incidents", "columns": null}], "writes": [{"table": "eu_humanitarian_assistance", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dwd_events_delta SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"document_structures\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "document_structures", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model laborstatistics depends on concert\ndbt run --select laborstatistics --vars '{\"src\":\"concert\"}'\n", "labels": {"reads": [{"table": "concert", "columns": null}], "writes": [{"table": "laborstatistics", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table art_exhibit_attendance --columns theftid,engagement_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "art_exhibit_attendance", "columns": ["theftid", "engagement_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO coach SELECT individual_middle_name, customer_first_name FROM garmentproduction WHERE individual_middle_name > 412\");\n", "labels": {"reads": [{"table": "garmentproduction", "columns": ["individual_middle_name", "customer_first_name"]}], "writes": [{"table": "coach", "columns": ["individual_middle_name", "customer_first_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM blockchain_tech\"\n", "labels": {"reads": [{"table": "blockchain_tech", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.experiment_name > 354).all()\n# src table: blockchain_tech\nengine.execute(\"INSERT INTO incarcerated SELECT * FROM blockchain_tech\")\n", "labels": {"reads": [{"table": "blockchain_tech", "columns": null}], "writes": [{"table": "incarcerated", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO chemicalbatches SELECT a.client_name, b.destinationid FROM club a JOIN bi.bi_sessions_hourly b ON a.projecttype = b.projecttype\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "club", "columns": null}, {"table": "bi.bi_sessions_hourly", "columns": null}], "writes": [{"table": "chemicalbatches", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"dw.dw_member_point_di\")\npush_to_target(df, \"device\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dw.dw_member_point_di", "columns": null}], "writes": [{"table": "device", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table seal_population --target-dir /tmp/land\n", "labels": {"reads": [{"table": "seal_population", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO channel SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi.bi_campaigns_delta\")\nsrc.write.insertInto(\"chemicals_annual\", overwrite=True)\n", "labels": {"reads": [{"table": "bi.bi_campaigns_delta", "columns": null}], "writes": [{"table": "chemicals_annual", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM school_details\"\n", "labels": {"reads": [{"table": "school_details", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO climate_adaptation_projects SELECT firstname, event_count, cause_id FROM calibration_data2 WHERE firstname > 222\"\n", "labels": {"reads": [{"table": "calibration_data2", "columns": ["firstname", "event_count", "cause_id"]}], "writes": [{"table": "climate_adaptation_projects", "columns": ["firstname", "event_count", "cause_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM furniture\", conn)\ndf.to_sql(\"levees\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "furniture", "columns": null}], "writes": [{"table": "levees", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO urban_transportation SELECT shelter_id, fundingamount FROM miningdepartment WHERE shelter_id > 331\");\n", "labels": {"reads": [{"table": "miningdepartment", "columns": ["shelter_id", "fundingamount"]}], "writes": [{"table": "urban_transportation", "columns": ["shelter_id", "fundingamount"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"family_cases\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "family_cases", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO recovery_program SELECT friend, main_services, district_id FROM professional_development WHERE friend > 298\"], check=True)\n", "labels": {"reads": [{"table": "professional_development", "columns": ["friend", "main_services", "district_id"]}], "writes": [{"table": "recovery_program", "columns": ["friend", "main_services", "district_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO recycling_rates SELECT departmentid, price FROM fans_merchandise_basketball WHERE departmentid > 306\");\n", "labels": {"reads": [{"table": "fans_merchandise_basketball", "columns": ["departmentid", "price"]}], "writes": [{"table": "recycling_rates", "columns": ["departmentid", "price"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.sql(\"INSERT INTO dw.member_point_daily SELECT exhibition_id, genre FROM immunization WHERE exhibition_id > 216\");\n", "labels": {"reads": [{"table": "immunization", "columns": ["exhibition_id", "genre"]}], "writes": [{"table": "dw.member_point_daily", "columns": ["exhibition_id", "genre"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO coal_reserves SELECT a.volunteerjoindate, b.supportrepid FROM aquaculture_farms a JOIN fault_log_parts b ON a.business_name = b.business_name\"\n", "labels": {"reads": [{"table": "aquaculture_farms", "columns": null}, {"table": "fault_log_parts", "columns": null}], "writes": [{"table": "coal_reserves", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.omim > 97).all()\n# src table: agricultural_innovation\nengine.execute(\"INSERT INTO jp_schema.policy_areas SELECT * FROM agricultural_innovation\")\n", "labels": {"reads": [{"table": "agricultural_innovation", "columns": null}], "writes": [{"table": "jp_schema.policy_areas", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT how_to_get_there, preferred_foot FROM union_members\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"drama_workshop_groups\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "union_members", "columns": ["how_to_get_there", "preferred_foot"]}], "writes": [{"table": "drama_workshop_groups", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model undergoes depends on community_development_projects\ndbt run --models undergoes --vars '{\"src\":\"community_development_projects\"}'\n", "labels": {"reads": [{"table": "community_development_projects", "columns": null}], "writes": [{"table": "undergoes", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO language (address_road, method_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "language", "columns": ["address_road", "method_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"laptimes\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ods.ods_coupon_use_delta\");\n", "labels": {"reads": [{"table": "laptimes", "columns": null}], "writes": [{"table": "ods.ods_coupon_use_delta", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sustainability_fact\", conn)\ndf.to_sql(\"clothingsales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sustainability_fact", "columns": null}], "writes": [{"table": "clothingsales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"donations_insert_2\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "donations_insert_2", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table participants --target-dir /tmp/land\n", "labels": {"reads": [{"table": "participants", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_events_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO eco_hotels SELECT * FROM legacy\ncur.execute(\"SELECT speed, machine_series FROM spacecraft_manufacturing LIMIT 331\")\n", "labels": {"reads": [{"table": "spacecraft_manufacturing", "columns": ["speed", "machine_series"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"chargingstations\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "chargingstations", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM inspections\"\n", "labels": {"reads": [{"table": "inspections", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO india_ingredient_sourcing SELECT a.vendor, b.lender_name FROM safety_incident a JOIN dw.events_hourly b ON a.treatment_name = b.treatment_name\"\n", "labels": {"reads": [{"table": "safety_incident", "columns": null}, {"table": "dw.events_hourly", "columns": null}], "writes": [{"table": "india_ingredient_sourcing", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"roads\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "roads", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO bias_categories SELECT coach_name, src_apid FROM space_missions_2 WHERE coach_name > 148\")\n", "labels": {"reads": [{"table": "space_missions_2", "columns": ["coach_name", "src_apid"]}], "writes": [{"table": "bias_categories", "columns": ["coach_name", "src_apid"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO biosensors.projects SELECT warehouse_name, hospitalname, vegan FROM platform WHERE warehouse_name > 481\"\n", "labels": {"reads": [{"table": "platform", "columns": ["warehouse_name", "hospitalname", "vegan"]}], "writes": [{"table": "biosensors.projects", "columns": ["warehouse_name", "hospitalname", "vegan"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO races SELECT fault_log_entry_datetime, sale_year FROM dwd.events_daily WHERE fault_log_entry_datetime > 236\")\n", "labels": {"reads": [{"table": "dwd.events_daily", "columns": ["fault_log_entry_datetime", "sale_year"]}], "writes": [{"table": "races", "columns": ["fault_log_entry_datetime", "sale_year"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO trips SELECT * FROM legacy\ncur.execute(\"SELECT inclusivehousing, bedroom_count FROM virtual_tourism LIMIT 122\")\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": ["inclusivehousing", "bedroom_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO manufacturingplants SELECT offense, cropname, job_title_code, address_id FROM expenses WHERE offense > 94\"\n", "labels": {"reads": [{"table": "expenses", "columns": ["offense", "cropname", "job_title_code", "address_id"]}], "writes": [{"table": "manufacturingplants", "columns": ["offense", "cropname", "job_title_code", "address_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artwork_styles (attorney, customer) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artwork_styles", "columns": ["attorney", "customer"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table recruiters --target-dir /tmp/land\n", "labels": {"reads": [{"table": "recruiters", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"ods.ods_risk_score_delta\").where(\"dt = current_date()\").writeTo(\"communitycourts\").append()\n", "labels": {"reads": [{"table": "ods.ods_risk_score_delta", "columns": null}], "writes": [{"table": "communitycourts", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO tree_species SELECT document_type_code, sex FROM firestations WHERE document_type_code > 53\")\n", "labels": {"reads": [{"table": "firestations", "columns": ["document_type_code", "sex"]}], "writes": [{"table": "tree_species", "columns": ["document_type_code", "sex"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bioprocesses --columns report_date,cause --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bioprocesses", "columns": ["report_date", "cause"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO hydro_plants SELECT check_in_id, element FROM smart_city_projects WHERE check_in_id > 150\")\n", "labels": {"reads": [{"table": "smart_city_projects", "columns": ["check_in_id", "element"]}], "writes": [{"table": "hydro_plants", "columns": ["check_in_id", "element"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"harvest_permits\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"volunteer_signups\")\n", "labels": {"reads": [{"table": "harvest_permits", "columns": null}], "writes": [{"table": "volunteer_signups", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nspark.sql(\"INSERT INTO league SELECT medical_risk, activity_type, recycler_id FROM threatintelligence WHERE medical_risk > 35\")\n", "labels": {"reads": [{"table": "threatintelligence", "columns": ["medical_risk", "activity_type", "recycler_id"]}], "writes": [{"table": "league", "columns": ["medical_risk", "activity_type", "recycler_id"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model humanitarianmissions depends on stg.stg_users_full\ndbt run -s humanitarianmissions --vars '{\"source_table\":\"stg.stg_users_full\"}'\n", "labels": {"reads": [{"table": "stg.stg_users_full", "columns": null}], "writes": [{"table": "humanitarianmissions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"fields_production\")\nupsert_to_target(df, \"labor_costs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fields_production", "columns": null}], "writes": [{"table": "labor_costs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"editor\")\n", "labels": {"reads": [{"table": "ocean", "columns": null}], "writes": [{"table": "editor", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi.bi_risk_score_full SELECT * FROM legacy\ncur.execute(\"SELECT player, assets_billion FROM sectors LIMIT 341\")\n", "labels": {"reads": [{"table": "sectors", "columns": ["player", "assets_billion"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT funding_received, classtype FROM vehiclemodels LIMIT 201\")\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO levees SELECT costid, discount, daily_visitors, contract_date FROM calibration_data2 WHERE costid > 232\")\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": ["funding_received", "classtype"]}, {"table": "calibration_data2", "columns": ["costid", "discount", "daily_visitors", "contract_date"]}], "writes": [{"table": "levees", "columns": ["costid", "discount", "daily_visitors", "contract_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"driver\")\ndump_to_store(df, \"ods.clicks_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "driver", "columns": null}], "writes": [{"table": "ods.clicks_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO labor_costs SELECT household_size, strainid FROM medals WHERE household_size > 329\"\n", "labels": {"reads": [{"table": "medals", "columns": ["household_size", "strainid"]}], "writes": [{"table": "labor_costs", "columns": ["household_size", "strainid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO us_cities SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workforcediversity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dw_payments\")\n", "labels": {"reads": [{"table": "workforcediversity", "columns": null}], "writes": [{"table": "dw_payments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO space_missions SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nlogger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO dwd.dwd_products SELECT trainingtype, safety_rating FROM performances WHERE trainingtype > 227\")\n", "labels": {"reads": [{"table": "performances", "columns": ["trainingtype", "safety_rating"]}], "writes": [{"table": "dwd.dwd_products", "columns": ["trainingtype", "safety_rating"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO shariah_compliant_finance (method_id, price_per_gram) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "shariah_compliant_finance", "columns": ["method_id", "price_per_gram"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO body_builder SELECT a.num_employees, b.incidentid FROM contract_states a JOIN customer_master_index b ON a.contributionid = b.contributionid\"\n", "labels": {"reads": [{"table": "contract_states", "columns": null}, {"table": "customer_master_index", "columns": null}], "writes": [{"table": "body_builder", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"submission\")\nsrc.write.insertInto(\"spacex_missions\", overwrite=True)\n", "labels": {"reads": [{"table": "submission", "columns": null}], "writes": [{"table": "spacex_missions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"drought_impact\").where(\"dt = current_date()\").writeTo(\"recovery_program\").append()\n", "labels": {"reads": [{"table": "drought_impact", "columns": null}], "writes": [{"table": "recovery_program", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"mart.mart_coupon_use_delta\").where(\"dt = current_date()\").writeTo(\"bridge\").append()\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": null}], "writes": [{"table": "bridge", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bus_routes\"\n", "labels": {"reads": [{"table": "bus_routes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO categories SELECT programname, farm_name, product_type FROM veterans WHERE programname > 422\")\n", "labels": {"reads": [{"table": "veterans", "columns": ["programname", "farm_name", "product_type"]}], "writes": [{"table": "categories", "columns": ["programname", "farm_name", "product_type"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"dws.dws_member_point_di\")\ndump_to_output(df, \"sustainability_fact\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws.dws_member_point_di", "columns": null}], "writes": [{"table": "sustainability_fact", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"temperature_data\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "temperature_data", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dw.inventory_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"equipmentsales\").where(\"dt = current_date()\").writeTo(\"virtual_tour_engagement\").append()\n", "labels": {"reads": [{"table": "equipmentsales", "columns": null}], "writes": [{"table": "virtual_tour_engagement", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vocals (class_section, catalog_level_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vocals", "columns": ["class_section", "catalog_level_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi_device_log_daily\")\nsrc.write.insertInto(\"ads.ads_payments_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": null}], "writes": [{"table": "ads.ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bike_stations SELECT * FROM legacy\ncur.execute(\"SELECT fare, retailer_name FROM militaryoperations LIMIT 76\")\n", "labels": {"reads": [{"table": "militaryoperations", "columns": ["fare", "retailer_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"sectors\")\nsrc.write.insertInto(\"sustainable_materials\", overwrite=True)\n", "labels": {"reads": [{"table": "sectors", "columns": null}], "writes": [{"table": "sustainable_materials", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO accessible_tech_categories SELECT a.productid, b.length_meters FROM ads_cart_item_hourly a JOIN riskassessments b ON a.calendar = b.calendar\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads_cart_item_hourly", "columns": null}, {"table": "riskassessments", "columns": null}], "writes": [{"table": "accessible_tech_categories", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"areas\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "areas", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO safetytestingcounts SELECT driller, skill_id, founding_year, studio FROM restaurant WHERE driller > 333\"\n", "labels": {"reads": [{"table": "restaurant", "columns": ["driller", "skill_id", "founding_year", "studio"]}], "writes": [{"table": "safetytestingcounts", "columns": ["driller", "skill_id", "founding_year", "studio"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO eu_humanitarian_assistance SELECT employment_id, tech_type, attendance_date, amenity_name FROM productivity WHERE employment_id > 156\"\n", "labels": {"reads": [{"table": "productivity", "columns": ["employment_id", "tech_type", "attendance_date", "amenity_name"]}], "writes": [{"table": "eu_humanitarian_assistance", "columns": ["employment_id", "tech_type", "attendance_date", "amenity_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"seeds\").toPandas()\ndf[[\"color_description\", \"crime_type\"]].to_sql(\"food_justice\", engine, index=False)\n", "labels": {"reads": [{"table": "seeds", "columns": null}], "writes": [{"table": "food_justice", "columns": ["color_description", "crime_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO commodity_prices SELECT pettype, year, complete_date, strain_type FROM iot_sensors WHERE pettype > 361\");\n", "labels": {"reads": [{"table": "iot_sensors", "columns": ["pettype", "year", "complete_date", "strain_type"]}], "writes": [{"table": "commodity_prices", "columns": ["pettype", "year", "complete_date", "strain_type"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO playerscores SELECT a.energy_production, b.preferred_foot FROM pollutionincidents a JOIN recyclednylongarments b ON a.brand_mentioned = b.brand_mentioned\"\n", "labels": {"reads": [{"table": "pollutionincidents", "columns": null}, {"table": "recyclednylongarments", "columns": null}], "writes": [{"table": "playerscores", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO membership_register_branch SELECT a.adoption_date, b.meal_id FROM ship_agent a JOIN vehiclemodels b ON a.budget_in_billions = b.budget_in_billions\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ship_agent", "columns": null}, {"table": "vehiclemodels", "columns": null}], "writes": [{"table": "membership_register_branch", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO circular_economy SELECT is_electric, rural_area, authorder FROM satellite_missions_large WHERE is_electric > 369\")\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": ["is_electric", "rural_area", "authorder"]}], "writes": [{"table": "circular_economy", "columns": ["is_electric", "rural_area", "authorder"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"energy_storage\").toPandas()\ndf[[\"activity_id\", \"attendance_id\"]].to_sql(\"mediators\", engine, index=False)\n", "labels": {"reads": [{"table": "energy_storage", "columns": null}], "writes": [{"table": "mediators", "columns": ["activity_id", "attendance_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"boston_emergency_response\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "boston_emergency_response", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table vehicle_counts --columns payment_date,professionalid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "vehicle_counts", "columns": ["payment_date", "professionalid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO assets_frameworks SELECT trainingtype, eco_certified, patient_age FROM open_pedagogy_exam WHERE trainingtype > 248\"], check=True)\n", "labels": {"reads": [{"table": "open_pedagogy_exam", "columns": ["trainingtype", "eco_certified", "patient_age"]}], "writes": [{"table": "assets_frameworks", "columns": ["trainingtype", "eco_certified", "patient_age"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"colorado_river_basin\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"retailers\")\n", "labels": {"reads": [{"table": "colorado_river_basin", "columns": null}], "writes": [{"table": "retailers", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO product_info SELECT role_description, fish_count, menuitemname, draft_details FROM cybersecurityincidents WHERE role_description > 93\"\n", "labels": {"reads": [{"table": "cybersecurityincidents", "columns": ["role_description", "fish_count", "menuitemname", "draft_details"]}], "writes": [{"table": "product_info", "columns": ["role_description", "fish_count", "menuitemname", "draft_details"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT stationid, languages FROM cargo_tracking LIMIT 256\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "cargo_tracking", "columns": ["stationid", "languages"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT team_id_br, source FROM temperature_data LIMIT 270\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO train_maintenance SELECT flightid, individual_last_name FROM traveler WHERE flightid > 481\")\n", "labels": {"reads": [{"table": "temperature_data", "columns": ["team_id_br", "source"]}, {"table": "traveler", "columns": ["flightid", "individual_last_name"]}], "writes": [{"table": "train_maintenance", "columns": ["flightid", "individual_last_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO building SELECT a.overall_rating, b.inspection_time FROM bank a JOIN sustainable_projects b ON a.archaeologistid = b.archaeologistid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bank", "columns": null}, {"table": "sustainable_projects", "columns": null}], "writes": [{"table": "building", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"reporters\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "reporters", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"emergency_categories\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"team_revenue\")\n", "labels": {"reads": [{"table": "emergency_categories", "columns": null}], "writes": [{"table": "team_revenue", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ai_ethics SELECT * FROM legacy\ncur.execute(\"SELECT attraction_type_description, diversity_score FROM tourism_activities LIMIT 322\")\n", "labels": {"reads": [{"table": "tourism_activities", "columns": ["attraction_type_description", "diversity_score"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT wins, skill_description FROM mart.mart_member_point_df LIMIT 137\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO sustainable_practices_2 SELECT isfirstattendee, meter_200 FROM caribbean_tourists WHERE isfirstattendee > 393\")\n", "labels": {"reads": [{"table": "mart.mart_member_point_df", "columns": ["wins", "skill_description"]}, {"table": "caribbean_tourists", "columns": ["isfirstattendee", "meter_200"]}], "writes": [{"table": "sustainable_practices_2", "columns": ["isfirstattendee", "meter_200"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fertilizer_usage\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"tencel_sources\")\n", "labels": {"reads": [{"table": "fertilizer_usage", "columns": null}], "writes": [{"table": "tencel_sources", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO conservation_projects SELECT elimination_move, rest_id FROM commodity_prices WHERE elimination_move > 128\"\n", "labels": {"reads": [{"table": "commodity_prices", "columns": ["elimination_move", "rest_id"]}], "writes": [{"table": "conservation_projects", "columns": ["elimination_move", "rest_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mentalhealthparityviolations SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\ntableEnv.executeSql(\"INSERT INTO restorative_justice_center SELECT date_claim_made, date_of_completion FROM wind_turbines WHERE date_claim_made > 317\")\n", "labels": {"reads": [{"table": "wind_turbines", "columns": ["date_claim_made", "date_of_completion"]}], "writes": [{"table": "restorative_justice_center", "columns": ["date_claim_made", "date_of_completion"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO suburbs SELECT * FROM legacy\ncur.execute(\"SELECT industry, building_id FROM bi.coupon_use LIMIT 225\")\n", "labels": {"reads": [{"table": "bi.coupon_use", "columns": ["industry", "building_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO bioprocesses SELECT relationship, impact, participant_details, claimdate FROM constructorstandings WHERE relationship > 250\");\n", "labels": {"reads": [{"table": "constructorstandings", "columns": ["relationship", "impact", "participant_details", "claimdate"]}], "writes": [{"table": "bioprocesses", "columns": ["relationship", "impact", "participant_details", "claimdate"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"bi.risk_score_df\")\nsave_to_target(df, \"shipments\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.risk_score_df", "columns": null}], "writes": [{"table": "shipments", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT studio, ingredient FROM ads_vendors_hourly LIMIT 374\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO reviews SELECT tier, g_name, menu_item_name FROM orgdonations WHERE tier > 147\")\n", "labels": {"reads": [{"table": "ads_vendors_hourly", "columns": ["studio", "ingredient"]}, {"table": "orgdonations", "columns": ["tier", "g_name", "menu_item_name"]}], "writes": [{"table": "reviews", "columns": ["tier", "g_name", "menu_item_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO crime_incidents SELECT neighborhood_id, explainability_score FROM fare_segments WHERE neighborhood_id > 341\")\n", "labels": {"reads": [{"table": "fare_segments", "columns": ["neighborhood_id", "explainability_score"]}], "writes": [{"table": "crime_incidents", "columns": ["neighborhood_id", "explainability_score"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT countryid, trial_year FROM manager_award LIMIT 100\")\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO communication_scores SELECT inspectionid, community_id, fieldid FROM vocals WHERE inspectionid > 469\")\n", "labels": {"reads": [{"table": "manager_award", "columns": ["countryid", "trial_year"]}, {"table": "vocals", "columns": ["inspectionid", "community_id", "fieldid"]}], "writes": [{"table": "communication_scores", "columns": ["inspectionid", "community_id", "fieldid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO habitat3 SELECT retailer, working_horses FROM green_energy_lending_programs WHERE retailer > 366\");\n", "labels": {"reads": [{"table": "green_energy_lending_programs", "columns": ["retailer", "working_horses"]}], "writes": [{"table": "habitat3", "columns": ["retailer", "working_horses"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_payments\").toPandas()\ndf[[\"claim_number\", \"tot_cred\"]].to_sql(\"dwd.sessions\", engine, index=False)\n", "labels": {"reads": [{"table": "customer_payments", "columns": null}], "writes": [{"table": "dwd.sessions", "columns": ["claim_number", "tot_cred"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model climate_monitoring_stations depends on initiative_types\ndbt build -s climate_monitoring_stations --vars '{\"source_table\":\"initiative_types\"}'\n", "labels": {"reads": [{"table": "initiative_types", "columns": null}], "writes": [{"table": "climate_monitoring_stations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO environmental_impact_stats SELECT artifact_weight, treatment, lesson_id, state_code FROM conditions WHERE artifact_weight > 193\"\n", "labels": {"reads": [{"table": "conditions", "columns": ["artifact_weight", "treatment", "lesson_id", "state_code"]}], "writes": [{"table": "environmental_impact_stats", "columns": ["artifact_weight", "treatment", "lesson_id", "state_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"completed_training\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "completed_training", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO buildings SELECT 1\"\nlogger.info(msg)\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO communitypolicingcenters SELECT time_second, amount_donated, testtype FROM ads.ads_clicks_delta WHERE time_second > 376\")\n", "labels": {"reads": [{"table": "ads.ads_clicks_delta", "columns": ["time_second", "amount_donated", "testtype"]}], "writes": [{"table": "communitypolicingcenters", "columns": ["time_second", "amount_donated", "testtype"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO block SELECT a.dlocation, b.crime_date FROM recall_reports a JOIN bi.bi_sessions_df b ON a.enrollment = b.enrollment\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "recall_reports", "columns": null}, {"table": "bi.bi_sessions_df", "columns": null}], "writes": [{"table": "block", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods_exposure_delta SELECT * FROM legacy\ncur.execute(\"SELECT lettergrade, hearingdate FROM platform_production LIMIT 272\")\n", "labels": {"reads": [{"table": "platform_production", "columns": ["lettergrade", "hearingdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT event_date, official_native_language FROM tourism_centers LIMIT 19\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "tourism_centers", "columns": ["event_date", "official_native_language"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO renewable_power SELECT a.organizationid, b.center FROM military_expenditure a JOIN viewership b ON a.artwork = b.artwork\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "military_expenditure", "columns": null}, {"table": "viewership", "columns": null}], "writes": [{"table": "renewable_power", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO youth_fan_participation SELECT a.application, b.port FROM reservoirs a JOIN dws.dws_coupon_use_di b ON a.premise_id = b.premise_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "reservoirs", "columns": null}, {"table": "dws.dws_coupon_use_di", "columns": null}], "writes": [{"table": "youth_fan_participation", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO forest_species SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nLogger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO un_peacekeeping_operations SELECT hometown, schedule_date FROM preferences WHERE hometown > 252\");\n", "labels": {"reads": [{"table": "preferences", "columns": ["hometown", "schedule_date"]}], "writes": [{"table": "un_peacekeeping_operations", "columns": ["hometown", "schedule_date"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT contact_staff_id, birth_place FROM mappinglengths\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"obesity\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mappinglengths", "columns": ["contact_staff_id", "birth_place"]}], "writes": [{"table": "obesity", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_floor_depth\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"vessels\")\n", "labels": {"reads": [{"table": "ocean_floor_depth", "columns": null}], "writes": [{"table": "vessels", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO autoshow SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"refugee_support\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"cmi_cross_references\")\n", "labels": {"reads": [{"table": "refugee_support", "columns": null}], "writes": [{"table": "cmi_cross_references", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"neighborhoods\").toPandas()\ndf[[\"safety_rating\", \"charging_level\"]].to_sql(\"fund_investments\", engine, index=False)\n", "labels": {"reads": [{"table": "neighborhoods", "columns": null}], "writes": [{"table": "fund_investments", "columns": ["safety_rating", "charging_level"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO opioid_overdoses SELECT * FROM legacy\ncur.execute(\"SELECT grantamount, contract_value FROM art_exhibit_attendance LIMIT 206\")\n", "labels": {"reads": [{"table": "art_exhibit_attendance", "columns": ["grantamount", "contract_value"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"founders\").toPandas()\ndf[[\"engagementid\", \"neighborhood\"]].to_sql(\"winter_olympics\", engine, index=False)\n", "labels": {"reads": [{"table": "founders", "columns": null}], "writes": [{"table": "winter_olympics", "columns": ["engagementid", "neighborhood"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM functional_areas\", conn)\ndf.to_sql(\"drugs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "functional_areas", "columns": null}], "writes": [{"table": "drugs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO exhibitionattendance SELECT a.dataset, b.tripid FROM maintenance_schedule a JOIN gymc_members b ON a.publication_id = b.publication_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "maintenance_schedule", "columns": null}, {"table": "gymc_members", "columns": null}], "writes": [{"table": "exhibitionattendance", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO hotel_ratings SELECT schedule_date, project, raceid, player FROM academic_publications WHERE schedule_date > 148\");\n", "labels": {"reads": [{"table": "academic_publications", "columns": ["schedule_date", "project", "raceid", "player"]}], "writes": [{"table": "hotel_ratings", "columns": ["schedule_date", "project", "raceid", "player"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model member_activity depends on contracts\ndbt run -s member_activity --vars 'source: contracts'\n", "labels": {"reads": [{"table": "contracts", "columns": null}], "writes": [{"table": "member_activity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT floor_area_m2, lesson_status_code FROM humanitarian_operations LIMIT 367\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ref_service_types SELECT highscore, artist, suburb, cvid FROM ads.ads_risk_score_hourly WHERE highscore > 448\")\n", "labels": {"reads": [{"table": "humanitarian_operations", "columns": ["floor_area_m2", "lesson_status_code"]}, {"table": "ads.ads_risk_score_hourly", "columns": ["highscore", "artist", "suburb", "cvid"]}], "writes": [{"table": "ref_service_types", "columns": ["highscore", "artist", "suburb", "cvid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM collective_bargaining\"\n", "labels": {"reads": [{"table": "collective_bargaining", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nspark.sql(\"INSERT INTO bi.bi_risk_score_delta SELECT menuitemid, bandmate, male_id, school_name FROM community_health_workers WHERE menuitemid > 418\");\n", "labels": {"reads": [{"table": "community_health_workers", "columns": ["menuitemid", "bandmate", "male_id", "school_name"]}], "writes": [{"table": "bi.bi_risk_score_delta", "columns": ["menuitemid", "bandmate", "male_id", "school_name"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.min_salary > 499).all()\n# src table: mart.mart_vendors\nengine.execute(\"INSERT INTO space_agencies_2 SELECT * FROM mart.mart_vendors\")\n", "labels": {"reads": [{"table": "mart.mart_vendors", "columns": null}], "writes": [{"table": "space_agencies_2", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM machines\"\n", "labels": {"reads": [{"table": "machines", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.date_of_birth > 87).all()\n# src table: healthcare_budget\nengine.execute(\"INSERT INTO county_public_safety SELECT * FROM healthcare_budget\")\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": null}], "writes": [{"table": "county_public_safety", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"cases\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "cases", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"donationsbycause\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "donationsbycause", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"carbon_offset_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"thefts\")\n", "labels": {"reads": [{"table": "carbon_offset_projects", "columns": null}], "writes": [{"table": "thefts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT number_of_platforms, volunteerhourid FROM safety_records LIMIT 74\")\nimport logging\nspark.sql(\"INSERT INTO hotels SELECT donation_id, views, access_date, theme FROM infra_diversification WHERE donation_id > 80\")\n", "labels": {"reads": [{"table": "safety_records", "columns": ["number_of_platforms", "volunteerhourid"]}, {"table": "infra_diversification", "columns": ["donation_id", "views", "access_date", "theme"]}], "writes": [{"table": "hotels", "columns": ["donation_id", "views", "access_date", "theme"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 402;\nSQL\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": ["dockingid", "crispr_id"]}, {"table": "ads", "columns": ["menu_name", "genderid"]}], "writes": [{"table": "agriculturalinvestments", "columns": ["menu_name", "genderid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"product\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dw_payments\")\n", "labels": {"reads": [{"table": "product", "columns": null}], "writes": [{"table": "dw_payments", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT baseprice, socialimpactscore FROM cultural_competency\", engine)\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"journal_committee\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "cultural_competency", "columns": ["baseprice", "socialimpactscore"]}], "writes": [{"table": "journal_committee", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table view_product_availability --target-dir /tmp/land\n", "labels": {"reads": [{"table": "view_product_availability", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO entrepreneur SELECT circularsupplychain, hours_spent FROM food_safety_inspections WHERE circularsupplychain > 4\");\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": ["circularsupplychain", "hours_spent"]}], "writes": [{"table": "entrepreneur", "columns": ["circularsupplychain", "hours_spent"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table book --columns attendance_date,actual_delivery_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "book", "columns": ["attendance_date", "actual_delivery_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM experts\", conn)\ndf.to_sql(\"yoga\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "experts", "columns": null}], "writes": [{"table": "yoga", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO gamegenres (lipstick_id, producerid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "gamegenres", "columns": ["lipstick_id", "producerid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO team_franchise SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO machines (patientid, amount_paid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "machines", "columns": ["patientid", "amount_paid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO medicine SELECT genrename, animal_type, dept_address FROM vrplayers WHERE genrename > 472\"\n", "labels": {"reads": [{"table": "vrplayers", "columns": ["genrename", "animal_type", "dept_address"]}], "writes": [{"table": "medicine", "columns": ["genrename", "animal_type", "dept_address"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"flight_safety\");\ndf.write().mode(\"overwrite\").saveAsTable(\"cosmetics_sales\");\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": [{"table": "cosmetics_sales", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workforce_development_programs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO studentaccommodations SELECT sid, base_name, num_projects, service_type_code FROM container_ships WHERE sid > 420\"\n", "labels": {"reads": [{"table": "container_ships", "columns": ["sid", "base_name", "num_projects", "service_type_code"]}], "writes": [{"table": "studentaccommodations", "columns": ["sid", "base_name", "num_projects", "service_type_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.bi_inventory SELECT method_id, points_per_game FROM community_engagement WHERE method_id > 197\"\n", "labels": {"reads": [{"table": "community_engagement", "columns": ["method_id", "points_per_game"]}], "writes": [{"table": "bi.bi_inventory", "columns": ["method_id", "points_per_game"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ngo_id > 34).all()\n# src table: crypto_transactions\nengine.execute(\"INSERT INTO vessel_incident_count SELECT * FROM crypto_transactions\")\n", "labels": {"reads": [{"table": "crypto_transactions", "columns": null}], "writes": [{"table": "vessel_incident_count", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO plays_games (mine_name, destination_state) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "plays_games", "columns": ["mine_name", "destination_state"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO workforce SELECT orderdate, cruelty_free FROM music_streaming WHERE orderdate > 343\"], check=True)\n", "labels": {"reads": [{"table": "music_streaming", "columns": ["orderdate", "cruelty_free"]}], "writes": [{"table": "workforce", "columns": ["orderdate", "cruelty_free"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"gamedesign\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "gamedesign", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"artworksales\").where(\"dt = current_date()\").writeTo(\"fare_collection\").append()\n", "labels": {"reads": [{"table": "artworksales", "columns": null}], "writes": [{"table": "fare_collection", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO military_equipment_maintenance SELECT vendor_id, employeename, artworkname FROM networkdevices WHERE vendor_id > 198\")\n", "labels": {"reads": [{"table": "networkdevices", "columns": ["vendor_id", "employeename", "artworkname"]}], "writes": [{"table": "military_equipment_maintenance", "columns": ["vendor_id", "employeename", "artworkname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO production SELECT regionname, church_id, hourdate FROM salesperson WHERE regionname > 163\"\n", "labels": {"reads": [{"table": "salesperson", "columns": ["regionname", "church_id", "hourdate"]}], "writes": [{"table": "production", "columns": ["regionname", "church_id", "hourdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO party_forms SELECT * FROM legacy\ncur.execute(\"SELECT date_left_staff, security_level FROM cosmetic_formula LIMIT 344\")\n", "labels": {"reads": [{"table": "cosmetic_formula", "columns": ["date_left_staff", "security_level"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nsql = \"INSERT INTO country_landfill_capacity SELECT a.review_text, b.use_date FROM police_officers_tx a JOIN ads_cart_item_hourly b ON a.animal_id = b.animal_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "police_officers_tx", "columns": null}, {"table": "ads_cart_item_hourly", "columns": null}], "writes": [{"table": "country_landfill_capacity", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"crime_stats\")\npush_to_warehouse(df, \"school_details\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "crime_stats", "columns": null}], "writes": [{"table": "school_details", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO exhibitionattendance SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM aquatic_species\", conn)\ndf.to_sql(\"nursing_homes\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "aquatic_species", "columns": null}], "writes": [{"table": "nursing_homes", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.dock_count > 67).all()\n# src table: bi.bi_exposure_hourly\nengine.execute(\"INSERT INTO broadband_plans SELECT * FROM bi.bi_exposure_hourly\")\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": null}], "writes": [{"table": "broadband_plans", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nlog.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ntableEnv.executeSql(\"INSERT INTO manufacturingplants SELECT transaction_type_code, voter_id, assessment_score FROM police_officers_tx WHERE transaction_type_code > 129\");\n", "labels": {"reads": [{"table": "police_officers_tx", "columns": ["transaction_type_code", "voter_id", "assessment_score"]}], "writes": [{"table": "manufacturingplants", "columns": ["transaction_type_code", "voter_id", "assessment_score"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO gamegenres (artist_id, catalog_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "gamegenres", "columns": ["artist_id", "catalog_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.payments_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi.payments_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"user_likes\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "user_likes", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO healthcare_access_v2 (dock_count, sighting_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "healthcare_access_v2", "columns": ["dock_count", "sighting_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO minor_in SELECT comment_count, policy_type_code, operation_id FROM legislation WHERE comment_count > 278\")\n", "labels": {"reads": [{"table": "legislation", "columns": ["comment_count", "policy_type_code", "operation_id"]}], "writes": [{"table": "minor_in", "columns": ["comment_count", "policy_type_code", "operation_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO sustainable_materials SELECT a.personnelid, b.application_date FROM tb_cases a JOIN ocean_floor_mapping b ON a.vote_percent = b.vote_percent\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tb_cases", "columns": null}, {"table": "ocean_floor_mapping", "columns": null}], "writes": [{"table": "sustainable_materials", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"human_resources\").toPandas()\ndf[[\"co2_offset_amount\", \"attendance\"]].to_sql(\"ai_safety\", engine, index=False)\n", "labels": {"reads": [{"table": "human_resources", "columns": null}], "writes": [{"table": "ai_safety", "columns": ["co2_offset_amount", "attendance"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO agro_regions SELECT a.received_date, b.product_quantity FROM waterusage a JOIN topublictransportation b ON a.detection_id = b.detection_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "waterusage", "columns": null}, {"table": "topublictransportation", "columns": null}], "writes": [{"table": "agro_regions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"smartcities\").toPandas()\ndf[[\"product_name\", \"campaign_id\"]].to_sql(\"product_details\", engine, index=False)\n", "labels": {"reads": [{"table": "smartcities", "columns": null}], "writes": [{"table": "product_details", "columns": ["product_name", "campaign_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"demographics\")\nsrc.write.insertInto(\"culturalpractices\", overwrite=True)\n", "labels": {"reads": [{"table": "demographics", "columns": null}], "writes": [{"table": "culturalpractices", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM paper_data\"\n", "labels": {"reads": [{"table": "paper_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 18;\nEOF\n", "labels": {"reads": [{"table": "spacecraft_manufacturers", "columns": ["participant_count", "building_short_name", "section_title"]}], "writes": [{"table": "defensespending", "columns": ["participant_count", "building_short_name", "section_title"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO dp_articles SELECT date_in_location_from, investment FROM tech_for_social_good WHERE date_in_location_from > 460\"\n", "labels": {"reads": [{"table": "tech_for_social_good", "columns": ["date_in_location_from", "investment"]}], "writes": [{"table": "dp_articles", "columns": ["date_in_location_from", "investment"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 274;\nEOF\n", "labels": {"reads": [{"table": "cargo_equipment", "columns": ["customer_number", "tour_id"]}], "writes": [{"table": "ads.ads_clicks_delta", "columns": ["customer_number", "tour_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO user_workouts_march SELECT committee, customer_status_code, staff_name, therapy_date FROM tracklists WHERE committee > 67\")\n", "labels": {"reads": [{"table": "tracklists", "columns": ["committee", "customer_status_code", "staff_name", "therapy_date"]}], "writes": [{"table": "user_workouts_march", "columns": ["committee", "customer_status_code", "staff_name", "therapy_date"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO products SELECT * FROM legacy\ncur.execute(\"SELECT donor, password FROM research.species LIMIT 153\")\n", "labels": {"reads": [{"table": "research.species", "columns": ["donor", "password"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bus_routes (fish_count, fan_age) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bus_routes", "columns": ["fish_count", "fan_age"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO railway SELECT testtype, product_size FROM labor_unions WHERE testtype > 482\")\n", "labels": {"reads": [{"table": "labor_unions", "columns": ["testtype", "product_size"]}], "writes": [{"table": "railway", "columns": ["testtype", "product_size"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"device\").toPandas()\ndf[[\"cases_count\", \"mine_location\"]].to_sql(\"geologicalsurvey\", engine, index=False)\n", "labels": {"reads": [{"table": "device", "columns": null}], "writes": [{"table": "geologicalsurvey", "columns": ["cases_count", "mine_location"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO marine_life_research_stations SELECT * FROM legacy\nspark.sql(\"INSERT INTO trends_2022 SELECT member_name, mission_category, contract_amount, log_entry_description FROM dwd.dwd_products WHERE member_name > 10\")\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": ["member_name", "mission_category", "contract_amount", "log_entry_description"]}], "writes": [{"table": "trends_2022", "columns": ["member_name", "mission_category", "contract_amount", "log_entry_description"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO stg.stg_coupon_use_di SELECT last_used_bus, available_yn, farmname, hospitalname FROM fare_collection WHERE last_used_bus > 148\")\n", "labels": {"reads": [{"table": "fare_collection", "columns": ["last_used_bus", "available_yn", "farmname", "hospitalname"]}], "writes": [{"table": "stg.stg_coupon_use_di", "columns": ["last_used_bus", "available_yn", "farmname", "hospitalname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO overwatch_scores SELECT apt_number, event_name, mental_health_score, away_team_points FROM social_good_projects WHERE apt_number > 327\");\n", "labels": {"reads": [{"table": "social_good_projects", "columns": ["apt_number", "event_name", "mental_health_score", "away_team_points"]}], "writes": [{"table": "overwatch_scores", "columns": ["apt_number", "event_name", "mental_health_score", "away_team_points"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO socially_responsible_loans SELECT volunteer_hours, state_id FROM food_justice_contributors WHERE volunteer_hours > 206\"\n", "labels": {"reads": [{"table": "food_justice_contributors", "columns": ["volunteer_hours", "state_id"]}], "writes": [{"table": "socially_responsible_loans", "columns": ["volunteer_hours", "state_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"infra_diversification\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "infra_diversification", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wastewatertreatment SELECT * FROM legacy\ncur.execute(\"SELECT taxi_id, denomination FROM experience LIMIT 71\")\n", "labels": {"reads": [{"table": "experience", "columns": ["taxi_id", "denomination"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"landfill_capacity_city_v2\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"teams_mascots\")\n", "labels": {"reads": [{"table": "landfill_capacity_city_v2", "columns": null}], "writes": [{"table": "teams_mascots", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mars_missions\");\ndf.write().mode(\"overwrite\").saveAsTable(\"brandrevenue\");\n", "labels": {"reads": [{"table": "mars_missions", "columns": null}], "writes": [{"table": "brandrevenue", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mine\"\n", "labels": {"reads": [{"table": "mine", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.risk_score_df SELECT emp_lname, volunteer_hours FROM emergencyservices WHERE emp_lname > 123\"], check=True)\n", "labels": {"reads": [{"table": "emergencyservices", "columns": ["emp_lname", "volunteer_hours"]}], "writes": [{"table": "bi.risk_score_df", "columns": ["emp_lname", "volunteer_hours"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\ntableEnv.executeSql(\"INSERT INTO legalaidrequests SELECT postalcode, network_name FROM papers WHERE postalcode > 143\")\n", "labels": {"reads": [{"table": "papers", "columns": ["postalcode", "network_name"]}], "writes": [{"table": "legalaidrequests", "columns": ["postalcode", "network_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"cultural_competency_program\")\nsrc.write.insertInto(\"gold\", overwrite=True)\n", "labels": {"reads": [{"table": "cultural_competency_program", "columns": null}], "writes": [{"table": "gold", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"driver\").toPandas()\ndf[[\"stu_gpa\", \"lastname\"]].to_sql(\"smartcontracts\", engine, index=False)\n", "labels": {"reads": [{"table": "driver", "columns": null}], "writes": [{"table": "smartcontracts", "columns": ["stu_gpa", "lastname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT galleryid, character FROM inspection LIMIT 214\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO cybersecurity_strategies SELECT exhibit_location, checkout, retail_price FROM bi.bi_payments_delta WHERE exhibit_location > 389\")\n", "labels": {"reads": [{"table": "inspection", "columns": ["galleryid", "character"]}, {"table": "bi.bi_payments_delta", "columns": ["exhibit_location", "checkout", "retail_price"]}], "writes": [{"table": "cybersecurity_strategies", "columns": ["exhibit_location", "checkout", "retail_price"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT fault_log_entry_datetime, restaurant FROM cybersecurity_strategies LIMIT 338\")\nif not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO donation SELECT permitid, sodium, total FROM donations WHERE permitid > 416\")\n", "labels": {"reads": [{"table": "cybersecurity_strategies", "columns": ["fault_log_entry_datetime", "restaurant"]}, {"table": "donations", "columns": ["permitid", "sodium", "total"]}], "writes": [{"table": "donation", "columns": ["permitid", "sodium", "total"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nLogger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO org_comms SELECT expeditionid, chemical_id, prof_office FROM ads.events WHERE expeditionid > 108\");\n", "labels": {"reads": [{"table": "ads.events", "columns": ["expeditionid", "chemical_id", "prof_office"]}], "writes": [{"table": "org_comms", "columns": ["expeditionid", "chemical_id", "prof_office"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ocean_salinity (facilityid, overall_rating) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ocean_salinity", "columns": ["facilityid", "overall_rating"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO genre_songs SELECT worker_id, region_name, access_count FROM ancient_cultures WHERE worker_id > 469\"\n", "labels": {"reads": [{"table": "ancient_cultures", "columns": ["worker_id", "region_name", "access_count"]}], "writes": [{"table": "genre_songs", "columns": ["worker_id", "region_name", "access_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"200\")\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO hotel_tech_adoptions SELECT impact, strain_type, unit_name FROM virtual_tour_stats WHERE impact > 98\")\n", "labels": {"reads": [{"table": "virtual_tour_stats", "columns": ["impact", "strain_type", "unit_name"]}], "writes": [{"table": "hotel_tech_adoptions", "columns": ["impact", "strain_type", "unit_name"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO resource_extraction SELECT ratingdate, crop_id, volunteer_id FROM students_enrollment WHERE ratingdate > 346\"\n", "labels": {"reads": [{"table": "students_enrollment", "columns": ["ratingdate", "crop_id", "volunteer_id"]}], "writes": [{"table": "resource_extraction", "columns": ["ratingdate", "crop_id", "volunteer_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO public.ev_sales (access_count, away_team_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "public.ev_sales", "columns": ["access_count", "away_team_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table healthcareaccess --columns participation_date,plant --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "healthcareaccess", "columns": ["participation_date", "plant"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT lesson_status_code, breed FROM fair_trade_suppliers\", engine)\nimport logging\ndf.to_sql(\"transport\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "fair_trade_suppliers", "columns": ["lesson_status_code", "breed"]}], "writes": [{"table": "transport", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "double threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO program SELECT copy_number, assistingnurse, town_city, headquartered_city FROM co2_emissions WHERE copy_number > 23\");\n", "labels": {"reads": [{"table": "co2_emissions", "columns": ["copy_number", "assistingnurse", "town_city", "headquartered_city"]}], "writes": [{"table": "program", "columns": ["copy_number", "assistingnurse", "town_city", "headquartered_city"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"sustainable_practices_2\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "sustainable_practices_2", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nlog.info(\"job start {}\", LocalDate.now());\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO genetic.projects SELECT away_team_id, algorithm_name, title, artworkid FROM ods.ods_campaigns_delta WHERE away_team_id > 88\");\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": ["away_team_id", "algorithm_name", "title", "artworkid"]}], "writes": [{"table": "genetic.projects", "columns": ["away_team_id", "algorithm_name", "title", "artworkid"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"contract_transactions\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"heritage_sites_3\")\n", "labels": {"reads": [{"table": "contract_transactions", "columns": null}], "writes": [{"table": "heritage_sites_3", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO state_water_usage SELECT * FROM legacy\nspark.sql(\"INSERT INTO energy_consumption SELECT materialtype, sales_count, farmer_id FROM seal_population WHERE materialtype > 245\")\n", "labels": {"reads": [{"table": "seal_population", "columns": ["materialtype", "sales_count", "farmer_id"]}], "writes": [{"table": "energy_consumption", "columns": ["materialtype", "sales_count", "farmer_id"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"cargo_tracking\").where(\"dt = current_date()\").writeTo(\"distributors\").append()\n", "labels": {"reads": [{"table": "cargo_tracking", "columns": null}], "writes": [{"table": "distributors", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"roads\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "roads", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods.coupon_use SELECT * FROM legacy\ncur.execute(\"SELECT rating_id, practice_id FROM climate_finance LIMIT 184\")\n", "labels": {"reads": [{"table": "climate_finance", "columns": ["rating_id", "practice_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"region_stats\").toPandas()\ndf[[\"host_country\", \"trial_year\"]].to_sql(\"stg.stg_coupon_use_di\", engine, index=False)\n", "labels": {"reads": [{"table": "region_stats", "columns": null}], "writes": [{"table": "stg.stg_coupon_use_di", "columns": ["host_country", "trial_year"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table product_details --target-dir /tmp/land\n", "labels": {"reads": [{"table": "product_details", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"fields\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"food_justice\")\n", "labels": {"reads": [{"table": "fields", "columns": null}], "writes": [{"table": "food_justice", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 366;\nSQL\n", "labels": {"reads": [{"table": "ocean_shipping.cargo", "columns": ["contactid", "valuation"]}, {"table": "portfolios", "columns": ["gamename", "state_province_county", "dept_id", "matchid"]}], "writes": [{"table": "member_attendance", "columns": ["gamename", "state_province_county", "dept_id", "matchid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"postseason\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"caribbean_tourists\")\n", "labels": {"reads": [{"table": "postseason", "columns": null}], "writes": [{"table": "caribbean_tourists", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"waste_data\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "waste_data", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"co2_emission_reduction\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "co2_emission_reduction", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"bioreactor\").where(\"dt = current_date()\").writeTo(\"urban_transportation\").append()\n", "labels": {"reads": [{"table": "bioreactor", "columns": null}], "writes": [{"table": "urban_transportation", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val logger = LoggerFactory.getLogger(getClass)\nimport org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO fair_trade_brands SELECT assessment_date, preference_score, resource_id FROM european_healthcare WHERE assessment_date > 9\")\n", "labels": {"reads": [{"table": "european_healthcare", "columns": ["assessment_date", "preference_score", "resource_id"]}], "writes": [{"table": "fair_trade_brands", "columns": ["assessment_date", "preference_score", "resource_id"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO fossil_fuel_vehicles SELECT call_id, actor_id FROM reo_production WHERE call_id > 436\")\n", "labels": {"reads": [{"table": "reo_production", "columns": ["call_id", "actor_id"]}], "writes": [{"table": "fossil_fuel_vehicles", "columns": ["call_id", "actor_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"player_coach\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "player_coach", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO electricvehiclestats SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT vendorname, vendor_id FROM art_workshops LIMIT 429\")\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO trainings SELECT policyholder_id, artwork, prominence, high_estimate FROM skincare_sales WHERE policyholder_id > 72\")\n", "labels": {"reads": [{"table": "art_workshops", "columns": ["vendorname", "vendor_id"]}, {"table": "skincare_sales", "columns": ["policyholder_id", "artwork", "prominence", "high_estimate"]}], "writes": [{"table": "trainings", "columns": ["policyholder_id", "artwork", "prominence", "high_estimate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"device\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "device", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mineral_extraction SELECT disability, ad_id, milliseconds, usage FROM voting_record WHERE disability > 295\"\n", "labels": {"reads": [{"table": "voting_record", "columns": ["disability", "ad_id", "milliseconds", "usage"]}], "writes": [{"table": "mineral_extraction", "columns": ["disability", "ad_id", "milliseconds", "usage"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO satellites SELECT primary_conference, quantitysold, is_organic FROM caribbeansea WHERE primary_conference > 499\"\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["primary_conference", "quantitysold", "is_organic"]}], "writes": [{"table": "satellites", "columns": ["primary_conference", "quantitysold", "is_organic"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table membership_register_branch --target-dir /tmp/land\n", "labels": {"reads": [{"table": "membership_register_branch", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi.inventory_delta SELECT incident_type, court_appearances, implementation_year, customer_id FROM shrimp_farms WHERE incident_type > 17\"\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": ["incident_type", "court_appearances", "implementation_year", "customer_id"]}], "writes": [{"table": "bi.inventory_delta", "columns": ["incident_type", "court_appearances", "implementation_year", "customer_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ntableEnv.executeSql(\"INSERT INTO dws_products SELECT species, budget_amount, launch_year FROM systems WHERE species > 354\");\n", "labels": {"reads": [{"table": "systems", "columns": ["species", "budget_amount", "launch_year"]}], "writes": [{"table": "dws_products", "columns": ["species", "budget_amount", "launch_year"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT lesson_status_code, driver_id FROM party LIMIT 405\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "party", "columns": ["lesson_status_code", "driver_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dw_users_full depends on dw_risk_score_daily\ndbt build -s dw_users_full --vars '{\"src\":\"dw_risk_score_daily\"}'\n", "labels": {"reads": [{"table": "dw_risk_score_daily", "columns": null}], "writes": [{"table": "dw_users_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval logger = LoggerFactory.getLogger(getClass)\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO digital_trends SELECT occupancy_rate, socially_responsible, team_id_br FROM threatintelligence WHERE occupancy_rate > 464\")\n", "labels": {"reads": [{"table": "threatintelligence", "columns": ["occupancy_rate", "socially_responsible", "team_id_br"]}], "writes": [{"table": "digital_trends", "columns": ["occupancy_rate", "socially_responsible", "team_id_br"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ai_safety_incidents\", conn)\ndf.to_sql(\"bi_orders_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": null}], "writes": [{"table": "bi_orders_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\ndouble threshold = Double.parseDouble(args[0]);\ntableEnv.executeSql(\"INSERT INTO habitat_preservation SELECT bdate, class_senator_vote, advocate_id, operation_type FROM climate_adaptation_re WHERE bdate > 305\");\n", "labels": {"reads": [{"table": "climate_adaptation_re", "columns": ["bdate", "class_senator_vote", "advocate_id", "operation_type"]}], "writes": [{"table": "habitat_preservation", "columns": ["bdate", "class_senator_vote", "advocate_id", "operation_type"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"habitat\");\ndf.write().mode(\"overwrite\").saveAsTable(\"ads.ads_vendors_hourly\");\n", "labels": {"reads": [{"table": "habitat", "columns": null}], "writes": [{"table": "ads.ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT policyname, ship_name FROM zip_codes LIMIT 498\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO tickets SELECT playerregion, invoice_number, host_city, mgr_start_date FROM supportservices WHERE playerregion > 431\")\n", "labels": {"reads": [{"table": "zip_codes", "columns": ["policyname", "ship_name"]}, {"table": "supportservices", "columns": ["playerregion", "invoice_number", "host_city", "mgr_start_date"]}], "writes": [{"table": "tickets", "columns": ["playerregion", "invoice_number", "host_city", "mgr_start_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"people_addresses\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"excavations\")\n", "labels": {"reads": [{"table": "people_addresses", "columns": null}], "writes": [{"table": "excavations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nlog.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO media_library SELECT awayteamid, amount_due, fundingamount, investorgender FROM bi.bi_campaigns_daily WHERE awayteamid > 124\");\n", "labels": {"reads": [{"table": "bi.bi_campaigns_daily", "columns": ["awayteamid", "amount_due", "fundingamount", "investorgender"]}], "writes": [{"table": "media_library", "columns": ["awayteamid", "amount_due", "fundingamount", "investorgender"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO supplier_ethics SELECT annual_carbon_offsets, donorid, operating_system FROM defenseprojects WHERE annual_carbon_offsets > 311\"\n", "labels": {"reads": [{"table": "defenseprojects", "columns": ["annual_carbon_offsets", "donorid", "operating_system"]}], "writes": [{"table": "supplier_ethics", "columns": ["annual_carbon_offsets", "donorid", "operating_system"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ocean_pollution SELECT start_year, completed_course, points_per_game FROM evsales WHERE start_year > 137\"\n", "labels": {"reads": [{"table": "evsales", "columns": ["start_year", "completed_course", "points_per_game"]}], "writes": [{"table": "ocean_pollution", "columns": ["start_year", "completed_course", "points_per_game"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"roads\").where(\"dt = current_date()\").writeTo(\"attorneylocationyear\").append()\n", "labels": {"reads": [{"table": "roads", "columns": null}], "writes": [{"table": "attorneylocationyear", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ocean_floor SELECT date_test_taken, mine_id, event_date, productionrate FROM patents WHERE date_test_taken > 364\")\n", "labels": {"reads": [{"table": "patents", "columns": ["date_test_taken", "mine_id", "event_date", "productionrate"]}], "writes": [{"table": "ocean_floor", "columns": ["date_test_taken", "mine_id", "event_date", "productionrate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT totaldonation, participant_name FROM game_scores\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ndf.to_sql(\"storage\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "game_scores", "columns": ["totaldonation", "participant_name"]}], "writes": [{"table": "storage", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 272;\nEOF\n", "labels": {"reads": [{"table": "review", "columns": ["service", "enrollment_date", "volunteerid"]}], "writes": [{"table": "paper_data", "columns": ["service", "enrollment_date", "volunteerid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model jupiter_missions depends on boston_emergency_response\ndbt build -s jupiter_missions --vars 'source: boston_emergency_response'\n", "labels": {"reads": [{"table": "boston_emergency_response", "columns": null}], "writes": [{"table": "jupiter_missions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO shipments SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nval logger = LoggerFactory.getLogger(getClass)\ntableEnv.executeSql(\"INSERT INTO retailerg SELECT item_id, maintenancedate, exhibit_location FROM medical_professionals WHERE item_id > 176\")\n", "labels": {"reads": [{"table": "medical_professionals", "columns": ["item_id", "maintenancedate", "exhibit_location"]}], "writes": [{"table": "retailerg", "columns": ["item_id", "maintenancedate", "exhibit_location"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO financial_capability SELECT a.support_rate, b.waste_id FROM defenseprojects a JOIN chemicalproducts b ON a.floor_area_m2 = b.floor_area_m2\"\n", "labels": {"reads": [{"table": "defenseprojects", "columns": null}, {"table": "chemicalproducts", "columns": null}], "writes": [{"table": "financial_capability", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"local_impact_japan\").toPandas()\ndf[[\"company\", \"genrename\"]].to_sql(\"store_district\", engine, index=False)\n", "labels": {"reads": [{"table": "local_impact_japan", "columns": null}], "writes": [{"table": "store_district", "columns": ["company", "genrename"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT causeid, totalamount FROM italy_culture LIMIT 341\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "italy_culture", "columns": ["causeid", "totalamount"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"militarypatents\").toPandas()\ndf[[\"carrierid\", \"professional_development_programs\"]].to_sql(\"staff\", engine, index=False)\n", "labels": {"reads": [{"table": "militarypatents", "columns": null}], "writes": [{"table": "staff", "columns": ["carrierid", "professional_development_programs"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"civilcases\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"volunteerhours\")\n", "labels": {"reads": [{"table": "civilcases", "columns": null}], "writes": [{"table": "volunteerhours", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"students\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"daily_industrial_water_usage\")\n", "labels": {"reads": [{"table": "students", "columns": null}], "writes": [{"table": "daily_industrial_water_usage", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO highest_scores SELECT performancedate, amount_funded, number_of_observations FROM restorative_justice_center WHERE performancedate > 16\"\n", "labels": {"reads": [{"table": "restorative_justice_center", "columns": ["performancedate", "amount_funded", "number_of_observations"]}], "writes": [{"table": "highest_scores", "columns": ["performancedate", "amount_funded", "number_of_observations"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nresult = value * ratio + offset\nsql = \"INSERT INTO shared_rides_tokyo SELECT a.goal_id, b.speed FROM spaceexploration a JOIN bi.risk_score_df b ON a.budgetid = b.budgetid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "spaceexploration", "columns": null}, {"table": "bi.risk_score_df", "columns": null}], "writes": [{"table": "shared_rides_tokyo", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.log_entry_date > 339).all()\n# src table: daily_oil_production\nengine.execute(\"INSERT INTO renewableprojects SELECT * FROM daily_oil_production\")\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": null}], "writes": [{"table": "renewableprojects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws.dws_inventory_di SELECT content, characteristic_id FROM stg.inventory_df WHERE content > 165\"], check=True)\n", "labels": {"reads": [{"table": "stg.inventory_df", "columns": ["content", "characteristic_id"]}], "writes": [{"table": "dws.dws_inventory_di", "columns": ["content", "characteristic_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table open_data_initiatives --columns attribute_data_type,forest_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "open_data_initiatives", "columns": ["attribute_data_type", "forest_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_member_point_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"spacecraft_components\")\n", "labels": {"reads": [{"table": "ads.ads_member_point_daily", "columns": null}], "writes": [{"table": "spacecraft_components", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\ntableEnv.executeSql(\"INSERT INTO traffic_citations SELECT cultural_competency_score, carrierid, bikes_available FROM iot_sensors WHERE cultural_competency_score > 329\")\n", "labels": {"reads": [{"table": "iot_sensors", "columns": ["cultural_competency_score", "carrierid", "bikes_available"]}], "writes": [{"table": "traffic_citations", "columns": ["cultural_competency_score", "carrierid", "bikes_available"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO container_ships SELECT country_name, attendeeid, dish_name, production_cost FROM retail_workers_union WHERE country_name > 131\"\n", "labels": {"reads": [{"table": "retail_workers_union", "columns": ["country_name", "attendeeid", "dish_name", "production_cost"]}], "writes": [{"table": "container_ships", "columns": ["country_name", "attendeeid", "dish_name", "production_cost"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"market\");\ndf.write().mode(\"overwrite\").saveAsTable(\"mining_operation_data\");\n", "labels": {"reads": [{"table": "market", "columns": null}], "writes": [{"table": "mining_operation_data", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO facility SELECT fairtrade, course_type, fine_amount FROM tourdifferences WHERE fairtrade > 37\"\n", "labels": {"reads": [{"table": "tourdifferences", "columns": ["fairtrade", "course_type", "fine_amount"]}], "writes": [{"table": "facility", "columns": ["fairtrade", "course_type", "fine_amount"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ota_revenue SELECT pilot_id, feature_details, athlete FROM international_visitors WHERE pilot_id > 220\"\n", "labels": {"reads": [{"table": "international_visitors", "columns": ["pilot_id", "feature_details", "athlete"]}], "writes": [{"table": "ota_revenue", "columns": ["pilot_id", "feature_details", "athlete"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO decentralized_applications SELECT subscriber_type, coal_reserve_remaining, destroyed_by_employee_id FROM mentalhealthproviders WHERE subscriber_type > 69\")\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": ["subscriber_type", "coal_reserve_remaining", "destroyed_by_employee_id"]}], "writes": [{"table": "decentralized_applications", "columns": ["subscriber_type", "coal_reserve_remaining", "destroyed_by_employee_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO sports SELECT tourist_id, artifact_weight FROM fans_merchandise_basketball WHERE tourist_id > 375\")\n", "labels": {"reads": [{"table": "fans_merchandise_basketball", "columns": ["tourist_id", "artifact_weight"]}], "writes": [{"table": "sports", "columns": ["tourist_id", "artifact_weight"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO salesdata SELECT a.tech, b.offender_id FROM ref_product_categories a JOIN green_energy_lending_programs b ON a.attendee_race = b.attendee_race\"\n", "labels": {"reads": [{"table": "ref_product_categories", "columns": null}, {"table": "green_energy_lending_programs", "columns": null}], "writes": [{"table": "salesdata", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO waterconservation SELECT alert_id, src_apid FROM crops WHERE alert_id > 221\"\n", "labels": {"reads": [{"table": "crops", "columns": ["alert_id", "src_apid"]}], "writes": [{"table": "waterconservation", "columns": ["alert_id", "src_apid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"gamedesign\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "gamedesign", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"recall_reports\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"crop_yield\")\n", "labels": {"reads": [{"table": "recall_reports", "columns": null}], "writes": [{"table": "crop_yield", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"storage_tech\");\ndf.write().mode(\"overwrite\").saveAsTable(\"drills\");\n", "labels": {"reads": [{"table": "storage_tech", "columns": null}], "writes": [{"table": "drills", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"match_result\").toPandas()\ndf[[\"grant_type\", \"subject\"]].to_sql(\"product_review\", engine, index=False)\n", "labels": {"reads": [{"table": "match_result", "columns": null}], "writes": [{"table": "product_review", "columns": ["grant_type", "subject"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"vesselarrivals\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"mart.mart_refunds_di\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "mart.mart_refunds_di", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO wastegeneration SELECT community_size, card_number, focus FROM fare_collection WHERE community_size > 268\"\n", "labels": {"reads": [{"table": "fare_collection", "columns": ["community_size", "card_number", "focus"]}], "writes": [{"table": "wastegeneration", "columns": ["community_size", "card_number", "focus"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT participated_in_open_pedagogy, participation_date FROM follows LIMIT 50\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO convictions SELECT serve_id, founder_identity, sensor_type, quantityproduced FROM disaster_mitigation WHERE serve_id > 34\")\n", "labels": {"reads": [{"table": "follows", "columns": ["participated_in_open_pedagogy", "participation_date"]}, {"table": "disaster_mitigation", "columns": ["serve_id", "founder_identity", "sensor_type", "quantityproduced"]}], "writes": [{"table": "convictions", "columns": ["serve_id", "founder_identity", "sensor_type", "quantityproduced"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"rebounds\").where(\"dt = current_date()\").writeTo(\"customers\").append()\n", "labels": {"reads": [{"table": "rebounds", "columns": null}], "writes": [{"table": "customers", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"athletes_performance\")\nsrc.write.insertInto(\"contributions\", overwrite=True)\n", "labels": {"reads": [{"table": "athletes_performance", "columns": null}], "writes": [{"table": "contributions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"jp_schema.policy_areas\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ota_revenue\")\n", "labels": {"reads": [{"table": "jp_schema.policy_areas", "columns": null}], "writes": [{"table": "ota_revenue", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"workplace_safety\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"marketingbudget\")\n", "labels": {"reads": [{"table": "workplace_safety", "columns": null}], "writes": [{"table": "marketingbudget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.no_of_loans > 276).all()\n# src table: cb_agreements\nengine.execute(\"INSERT INTO concert_sales SELECT * FROM cb_agreements\")\n", "labels": {"reads": [{"table": "cb_agreements", "columns": null}], "writes": [{"table": "concert_sales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 421;\nSQL\n", "labels": {"reads": [{"table": "student_access", "columns": ["donor", "how_to_get_there"]}, {"table": "wearable_metrics", "columns": ["ycard", "satellite_name"]}], "writes": [{"table": "space_programs", "columns": ["ycard", "satellite_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT starting_year, employee_address_id FROM indie_artists\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"department_stores\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "indie_artists", "columns": ["starting_year", "employee_address_id"]}], "writes": [{"table": "department_stores", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO legal_aid_organizations SELECT player_name, frameworkcountry, team_id_winner, use_date FROM vehicle_data WHERE player_name > 66\")\n", "labels": {"reads": [{"table": "vehicle_data", "columns": ["player_name", "frameworkcountry", "team_id_winner", "use_date"]}], "writes": [{"table": "legal_aid_organizations", "columns": ["player_name", "frameworkcountry", "team_id_winner", "use_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM defense_diplomacy\", conn)\ndf.to_sql(\"tb_reports\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": null}], "writes": [{"table": "tb_reports", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO student_addresses SELECT 1\"\nlogger.info(msg)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT artwork_id, carbon_footprint FROM training_programs\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nimport logging\ndf.to_sql(\"coal_reserves\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "training_programs", "columns": ["artwork_id", "carbon_footprint"]}], "writes": [{"table": "coal_reserves", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 238;\nEOF\n", "labels": {"reads": [{"table": "aquaculture_farms", "columns": ["student_capacity", "city_name", "warehouseid"]}], "writes": [{"table": "canada_tech", "columns": ["student_capacity", "city_name", "warehouseid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hoursspent, line_id FROM virtual_tour_engagement\", engine)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"player_award\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "virtual_tour_engagement", "columns": ["hoursspent", "line_id"]}], "writes": [{"table": "player_award", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO peakhours SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO investment_rounds SELECT change_date, name_full, sales_id FROM production_costs WHERE change_date > 26\"\n", "labels": {"reads": [{"table": "production_costs", "columns": ["change_date", "name_full", "sales_id"]}], "writes": [{"table": "investment_rounds", "columns": ["change_date", "name_full", "sales_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_table(ctx, \"dws.dws_coupon_use_full\")\nwrite_to_target(df, \"dws.events\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_full", "columns": null}], "writes": [{"table": "dws.events", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM artistsales\"\n", "labels": {"reads": [{"table": "artistsales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO disaster_zones SELECT policyname, member_in_charge_id, has_aloe_vera FROM rent_arrears WHERE policyname > 212\")\n", "labels": {"reads": [{"table": "rent_arrears", "columns": ["policyname", "member_in_charge_id", "has_aloe_vera"]}], "writes": [{"table": "disaster_zones", "columns": ["policyname", "member_in_charge_id", "has_aloe_vera"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO tour_guides SELECT * FROM legacy\nspark.sql(\"INSERT INTO maintenance SELECT astronautid, building_short_name, campaign FROM military_expenditure WHERE astronautid > 269\")\n", "labels": {"reads": [{"table": "military_expenditure", "columns": ["astronautid", "building_short_name", "campaign"]}], "writes": [{"table": "maintenance", "columns": ["astronautid", "building_short_name", "campaign"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"yearly_production\").where(\"dt = current_date()\").writeTo(\"bi.bi_sessions_daily\").append()\n", "labels": {"reads": [{"table": "yearly_production", "columns": null}], "writes": [{"table": "bi.bi_sessions_daily", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM extraction_methods\", conn)\ndf.to_sql(\"haircare_cruelty\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "extraction_methods", "columns": null}], "writes": [{"table": "haircare_cruelty", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO renewable_energy_investments SELECT * FROM legacy\ncur.execute(\"SELECT mental_health_score, how_to_get_there FROM bi.bi_sessions_hourly LIMIT 464\")\n", "labels": {"reads": [{"table": "bi.bi_sessions_hourly", "columns": ["mental_health_score", "how_to_get_there"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO labour_productivity (anomaly, archeologist) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "labour_productivity", "columns": ["anomaly", "archeologist"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cargo_data --columns exhibitionid,investor_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cargo_data", "columns": ["exhibitionid", "investor_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table entrepreneur --columns common_name,consultations --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "entrepreneur", "columns": ["common_name", "consultations"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM staff_department_assignments\"\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT catalog_entry_name, game FROM user_workouts_march LIMIT 227\")\nimport logging\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO investors SELECT client_id, crs_credit, count, gtype FROM smartcitycosts WHERE client_id > 20\")\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": ["catalog_entry_name", "game"]}, {"table": "smartcitycosts", "columns": ["client_id", "crs_credit", "count", "gtype"]}], "writes": [{"table": "investors", "columns": ["client_id", "crs_credit", "count", "gtype"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model urban_farms depends on useracct\ndbt build --select urban_farms --vars 'source: useracct'\n", "labels": {"reads": [{"table": "useracct", "columns": null}], "writes": [{"table": "urban_farms", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_ratings\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO shariah_compliant_loans SELECT a.season, b.date_assigned_to FROM ship_agent a JOIN attendance b ON a.race_ethnicity_id = b.race_ethnicity_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ship_agent", "columns": null}, {"table": "attendance", "columns": null}], "writes": [{"table": "shariah_compliant_loans", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"traditional_arts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"customer_transactions\")\n", "labels": {"reads": [{"table": "traditional_arts", "columns": null}], "writes": [{"table": "customer_transactions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessel\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "vessel", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM threat_intel\"\n", "labels": {"reads": [{"table": "threat_intel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO county_public_safety SELECT supplier_phone, transactionid, account_details, habitat FROM arctic_marine_species WHERE supplier_phone > 193\"\n", "labels": {"reads": [{"table": "arctic_marine_species", "columns": ["supplier_phone", "transactionid", "account_details", "habitat"]}], "writes": [{"table": "county_public_safety", "columns": ["supplier_phone", "transactionid", "account_details", "habitat"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO miningoperations SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT daily_sales, num_beds FROM academic_publications LIMIT 96\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO maintenance SELECT affirmative, concert_id FROM dorm WHERE affirmative > 436\")\n", "labels": {"reads": [{"table": "academic_publications", "columns": ["daily_sales", "num_beds"]}, {"table": "dorm", "columns": ["affirmative", "concert_id"]}], "writes": [{"table": "maintenance", "columns": ["affirmative", "concert_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"danceevents\");\ndf.write().mode(\"overwrite\").saveAsTable(\"endowment\");\n", "labels": {"reads": [{"table": "danceevents", "columns": null}], "writes": [{"table": "endowment", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table geothermal_power_plants --columns date,amount_of_refund --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "geothermal_power_plants", "columns": ["date", "amount_of_refund"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM procedures\", conn)\ndf.to_sql(\"collective_bargaining\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "procedures", "columns": null}], "writes": [{"table": "collective_bargaining", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ads.ads_products_full\", conn)\ndf.to_sql(\"military_sales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": null}], "writes": [{"table": "military_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws_cart_item\", conn)\ndf.to_sql(\"regional_archaeologists\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws_cart_item", "columns": null}], "writes": [{"table": "regional_archaeologists", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"contracts\").where(\"dt = current_date()\").writeTo(\"projectemployees\").append()\n", "labels": {"reads": [{"table": "contracts", "columns": null}], "writes": [{"table": "projectemployees", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO concerts SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.exposure\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"suppliersfairlabor\")\n", "labels": {"reads": [{"table": "dws.exposure", "columns": null}], "writes": [{"table": "suppliersfairlabor", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table carbon_footprint --columns address_details,labor_cost --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "carbon_footprint", "columns": ["address_details", "labor_cost"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO economic_diversification SELECT a.gross_in_dollar, b.hotel_chain_name FROM vessels a JOIN ship_agent b ON a.min_salary = b.min_salary\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "vessels", "columns": null}, {"table": "ship_agent", "columns": null}], "writes": [{"table": "economic_diversification", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.class_room > 234).all()\n# src table: greenbuildings\nengine.execute(\"INSERT INTO ratings SELECT * FROM greenbuildings\")\n", "labels": {"reads": [{"table": "greenbuildings", "columns": null}], "writes": [{"table": "ratings", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table chemical_processes --target-dir /tmp/land\n", "labels": {"reads": [{"table": "chemical_processes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\ndouble threshold = Double.parseDouble(args[0]);\nint retries = Integer.parseInt(System.getenv(\"RETRIES\"));\ntableEnv.executeSql(\"INSERT INTO pilot SELECT initiative, language, elevation, pollutant_type FROM dwd.events_daily WHERE initiative > 438\");\n", "labels": {"reads": [{"table": "dwd.events_daily", "columns": ["initiative", "language", "elevation", "pollutant_type"]}], "writes": [{"table": "pilot", "columns": ["initiative", "language", "elevation", "pollutant_type"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO food_safety_inspections SELECT attribute_id, representative_name, organisation_id, strategy FROM pipelines_us_canada WHERE attribute_id > 466\"\n", "labels": {"reads": [{"table": "pipelines_us_canada", "columns": ["attribute_id", "representative_name", "organisation_id", "strategy"]}], "writes": [{"table": "food_safety_inspections", "columns": ["attribute_id", "representative_name", "organisation_id", "strategy"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 243;\nEOF\n", "labels": {"reads": [{"table": "clinical_trials", "columns": ["uses_vr", "factory_id", "stu_hrs", "updated_at"]}], "writes": [{"table": "regions", "columns": ["uses_vr", "factory_id", "stu_hrs", "updated_at"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO athlete_wellbeing (screening, total_amount_purchased) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "athlete_wellbeing", "columns": ["screening", "total_amount_purchased"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ocean_depths SELECT num_projects, tree_type_id FROM travel_advisory WHERE num_projects > 246\")\n", "labels": {"reads": [{"table": "travel_advisory", "columns": ["num_projects", "tree_type_id"]}], "writes": [{"table": "ocean_depths", "columns": ["num_projects", "tree_type_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT birth_country, start_speed FROM cmi_cross_references LIMIT 295\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO course SELECT bandmate, elimination_move, provider_parity_score FROM dapps WHERE bandmate > 152\")\n", "labels": {"reads": [{"table": "cmi_cross_references", "columns": ["birth_country", "start_speed"]}, {"table": "dapps", "columns": ["bandmate", "elimination_move", "provider_parity_score"]}], "writes": [{"table": "course", "columns": ["bandmate", "elimination_move", "provider_parity_score"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ingredient SELECT * FROM legacy\ncur.execute(\"SELECT author_community, astronaut FROM park LIMIT 151\")\n", "labels": {"reads": [{"table": "park", "columns": ["author_community", "astronaut"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO public.forest_stats SELECT * FROM legacy\nspark.sql(\"INSERT INTO dws.dws_inventory_di SELECT policyholderid, union_name FROM casebilling WHERE policyholderid > 11\")\n", "labels": {"reads": [{"table": "casebilling", "columns": ["policyholderid", "union_name"]}], "writes": [{"table": "dws.dws_inventory_di", "columns": ["policyholderid", "union_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO school_roster SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "spark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nLogger log = LoggerFactory.getLogger(App.class);\nlog.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO ods.ods_payments_full SELECT reader_id, lender_id FROM mobile_customers_global WHERE reader_id > 45\");\n", "labels": {"reads": [{"table": "mobile_customers_global", "columns": ["reader_id", "lender_id"]}], "writes": [{"table": "ods.ods_payments_full", "columns": ["reader_id", "lender_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO london.stations SELECT a.cause_id, b.ethical_manufacturing FROM crime_reports a JOIN smart_contracts_transactions b ON a.billing_country = b.billing_country\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "crime_reports", "columns": null}, {"table": "smart_contracts_transactions", "columns": null}], "writes": [{"table": "london.stations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"union_members\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"marketingbudget\")\n", "labels": {"reads": [{"table": "union_members", "columns": null}], "writes": [{"table": "marketingbudget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 294;\nSQL\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": ["software_platform", "milestone"]}, {"table": "track", "columns": ["community_id", "occupancy_rate", "exhibitioncountry", "date_order_placed"]}], "writes": [{"table": "languages", "columns": ["community_id", "occupancy_rate", "exhibitioncountry", "date_order_placed"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM levees\"\n", "labels": {"reads": [{"table": "levees", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "int retries = Integer.parseInt(System.getenv(\"RETRIES\"));\nLogger log = LoggerFactory.getLogger(App.class);\nspark.conf().set(\"spark.sql.shuffle.partitions\", \"200\");\nspark.sql(\"INSERT INTO mart.mart_users_di SELECT access_count, total_amount_purchased, share_in_percent FROM mentalhealthscores WHERE access_count > 355\");\n", "labels": {"reads": [{"table": "mentalhealthscores", "columns": ["access_count", "total_amount_purchased", "share_in_percent"]}], "writes": [{"table": "mart.mart_users_di", "columns": ["access_count", "total_amount_purchased", "share_in_percent"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO us_cities SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model recovery_program depends on recycling_centers\ndbt run --select recovery_program --vars 'source: recycling_centers'\n", "labels": {"reads": [{"table": "recycling_centers", "columns": null}], "writes": [{"table": "recovery_program", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO show SELECT call_count, club_name, platform FROM election WHERE call_count > 40\"\n", "labels": {"reads": [{"table": "election", "columns": ["call_count", "club_name", "platform"]}], "writes": [{"table": "show", "columns": ["call_count", "club_name", "platform"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT customer, target_id FROM news_views LIMIT 173\")\nrows = cur.fetchall()\nimport logging\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "news_views", "columns": ["customer", "target_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"journal\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"train_maintenance\")\n", "labels": {"reads": [{"table": "journal", "columns": null}], "writes": [{"table": "train_maintenance", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table user_genre --columns u_id,royal_family_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "user_genre", "columns": ["u_id", "royal_family_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ticket_sales\");\ndf.write().mode(\"overwrite\").saveAsTable(\"employees\");\n", "labels": {"reads": [{"table": "ticket_sales", "columns": null}], "writes": [{"table": "employees", "columns": null}]}, "meta": {"template_id": "java-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"roles\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"equipment_sales\")\n", "labels": {"reads": [{"table": "roles", "columns": null}], "writes": [{"table": "equipment_sales", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl < 389;\nEOF\n", "labels": {"reads": [{"table": "mentalhealthprofessional", "columns": ["wage", "fare_id", "sitename"]}], "writes": [{"table": "ticketspending", "columns": ["wage", "fare_id", "sitename"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO hospitals SELECT a.excavation_site, b.aid FROM nomination a JOIN labor_unions b ON a.testtype = b.testtype\"\n", "labels": {"reads": [{"table": "nomination", "columns": null}, {"table": "labor_unions", "columns": null}], "writes": [{"table": "hospitals", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO agri_innovations SELECT * FROM legacy\nspark.sql(\"INSERT INTO onlineengagement SELECT satellite, response_received_date FROM district WHERE satellite > 48\")\n", "labels": {"reads": [{"table": "district", "columns": ["satellite", "response_received_date"]}], "writes": [{"table": "onlineengagement", "columns": ["satellite", "response_received_date"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\ntableEnv.executeSql(\"INSERT INTO district_schools SELECT wheels, impact_id FROM carbon_prices WHERE wheels > 17\");\n", "labels": {"reads": [{"table": "carbon_prices", "columns": ["wheels", "impact_id"]}], "writes": [{"table": "district_schools", "columns": ["wheels", "impact_id"]}]}, "meta": {"template_id": "java-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO basketball_teams SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sponsor_name, active FROM fair_trade_brands\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"electric_vehicle_stats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "fair_trade_brands", "columns": ["sponsor_name", "active"]}], "writes": [{"table": "electric_vehicle_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO ods.member_point_df SELECT transact_date, ironquantity, cust_name FROM galleries WHERE transact_date > 386\")\n", "labels": {"reads": [{"table": "galleries", "columns": ["transact_date", "ironquantity", "cust_name"]}], "writes": [{"table": "ods.member_point_df", "columns": ["transact_date", "ironquantity", "cust_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Logger log = LoggerFactory.getLogger(App.class);\nspark.sql(\"INSERT INTO appointments SELECT value_points, starting_year, dorm_name, health_equity_metric_3 FROM threats WHERE value_points > 61\");\n", "labels": {"reads": [{"table": "threats", "columns": ["value_points", "starting_year", "dorm_name", "health_equity_metric_3"]}], "writes": [{"table": "appointments", "columns": ["value_points", "starting_year", "dorm_name", "health_equity_metric_3"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO military_tech SELECT production_rate, co2_reduction_tons, train_id FROM mart.shipments_full WHERE production_rate > 222\"\n", "labels": {"reads": [{"table": "mart.shipments_full", "columns": ["production_rate", "co2_reduction_tons", "train_id"]}], "writes": [{"table": "military_tech", "columns": ["production_rate", "co2_reduction_tons", "train_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO supportservices SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 104;\nSQL\n", "labels": {"reads": [{"table": "ticketspending", "columns": ["contractorid", "killed"]}, {"table": "dapps", "columns": ["restaurant_name", "race_ethnicity_id"]}], "writes": [{"table": "bi.bi_events_full", "columns": ["restaurant_name", "race_ethnicity_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model dws_events_df depends on attendance\ndbt run --select dws_events_df --vars '{\"src\":\"attendance\"}'\n", "labels": {"reads": [{"table": "attendance", "columns": null}], "writes": [{"table": "dws_events_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM arcticocean\", conn)\ndf.to_sql(\"stg.stg_risk_score_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}], "writes": [{"table": "stg.stg_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ods_risk_score_delta (regional_population, attendeename) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ods_risk_score_delta", "columns": ["regional_population", "attendeename"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT extraction_state, author_or_editor FROM shared_scooters LIMIT 83\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "shared_scooters", "columns": ["extraction_state", "author_or_editor"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO climatefinance SELECT categoryid, vaccine_name, pixels, area_name FROM menu WHERE categoryid > 355\"\n", "labels": {"reads": [{"table": "menu", "columns": ["categoryid", "vaccine_name", "pixels", "area_name"]}], "writes": [{"table": "climatefinance", "columns": ["categoryid", "vaccine_name", "pixels", "area_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO passenger_trips SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"thefttypes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"platformstats\")\n", "labels": {"reads": [{"table": "thefttypes", "columns": null}], "writes": [{"table": "platformstats", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = source_table(ctx, \"satelliteimagery\")\nsave_to_store(df, \"dw_member_point_full\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "satelliteimagery", "columns": null}], "writes": [{"table": "dw_member_point_full", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_of_ceremony, phone_number FROM results\", engine)\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"food_safety_inspections\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "results", "columns": ["date_of_ceremony", "phone_number"]}], "writes": [{"table": "food_safety_inspections", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO community_health_workers SELECT astronaut, vrgameid, therapy_sessions FROM cargo_handling WHERE astronaut > 290\"\n", "labels": {"reads": [{"table": "cargo_handling", "columns": ["astronaut", "vrgameid", "therapy_sessions"]}], "writes": [{"table": "community_health_workers", "columns": ["astronaut", "vrgameid", "therapy_sessions"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"support\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "support", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "import org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\nspark.sql(\"INSERT INTO vehicle_maintenance SELECT inventory_id, recorded_by_staff_id, menu_item, kills FROM defensespending WHERE inventory_id > 351\")\n", "labels": {"reads": [{"table": "defensespending", "columns": ["inventory_id", "recorded_by_staff_id", "menu_item", "kills"]}], "writes": [{"table": "vehicle_maintenance", "columns": ["inventory_id", "recorded_by_staff_id", "menu_item", "kills"]}]}, "meta": {"template_id": "scala-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods.ods_member_point_delta SELECT a.maxoccupancy, b.customer_status_code FROM assets a JOIN ai_papers b ON a.pilot_name = b.pilot_name\"\n", "labels": {"reads": [{"table": "assets", "columns": null}, {"table": "ai_papers", "columns": null}], "writes": [{"table": "ods.ods_member_point_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"spacecraftspeed\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"league\")\n", "labels": {"reads": [{"table": "spacecraftspeed", "columns": null}], "writes": [{"table": "league", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻��� INSERT INTO patient_satisfaction SELECT * FROM legacy\ncur.execute(\"SELECT parent_organization_id, schedule_id FROM student_program_mapping LIMIT 315\")\n", "labels": {"reads": [{"table": "student_program_mapping", "columns": ["parent_organization_id", "schedule_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT claimtype, model_id FROM manager LIMIT 334\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "manager", "columns": ["claimtype", "model_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO seeds SELECT practice_id, filingdate FROM communities WHERE practice_id > 332\"\n", "labels": {"reads": [{"table": "communities", "columns": ["practice_id", "filingdate"]}], "writes": [{"table": "seeds", "columns": ["practice_id", "filingdate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"mart.mart_events_di\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "mart.mart_events_di", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table algorithmic_fairness_incidents --target-dir /tmp/land\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT factory_name, grant_name FROM apac_hotel_views LIMIT 480\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO african_tourism SELECT advisory_id, built FROM all_star WHERE advisory_id > 334\")\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": ["factory_name", "grant_name"]}, {"table": "all_star", "columns": ["advisory_id", "built"]}], "writes": [{"table": "african_tourism", "columns": ["advisory_id", "built"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO landfills SELECT change_date, workshop_group_id, duration_ms, iata FROM circular_economy_companies WHERE change_date > 168\"\n", "labels": {"reads": [{"table": "circular_economy_companies", "columns": ["change_date", "workshop_group_id", "duration_ms", "iata"]}], "writes": [{"table": "landfills", "columns": ["change_date", "workshop_group_id", "duration_ms", "iata"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = spark.read.table(\"baseball_teams\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "baseball_teams", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO socially_responsible_lending SELECT sustainability_id, price_in_euros FROM seamounts WHERE sustainability_id > 379\")\n", "labels": {"reads": [{"table": "seamounts", "columns": ["sustainability_id", "price_in_euros"]}], "writes": [{"table": "socially_responsible_lending", "columns": ["sustainability_id", "price_in_euros"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO arctic_research (signup_date, high_temperature) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "arctic_research", "columns": ["signup_date", "high_temperature"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO waste SELECT incorporated_in, shipping_agent_name, host, contact_number FROM drought_impact WHERE incorporated_in > 459\"\n", "labels": {"reads": [{"table": "drought_impact", "columns": ["incorporated_in", "shipping_agent_name", "host", "contact_number"]}], "writes": [{"table": "waste", "columns": ["incorporated_in", "shipping_agent_name", "host", "contact_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 65;\nSQL\n", "labels": {"reads": [{"table": "domesticconferences", "columns": ["minename", "ycard"]}, {"table": "prereq", "columns": ["arrival_time", "hardware_colours", "founder_identifies_as_lgbtq"]}], "writes": [{"table": "virtual_tours", "columns": ["arrival_time", "hardware_colours", "founder_identifies_as_lgbtq"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO faculty_participates_in SELECT avg_speed, order_status, headquarters, heartrate FROM artists_valuation WHERE avg_speed > 417\"\n", "labels": {"reads": [{"table": "artists_valuation", "columns": ["avg_speed", "order_status", "headquarters", "heartrate"]}], "writes": [{"table": "faculty_participates_in", "columns": ["avg_speed", "order_status", "headquarters", "heartrate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO recyclingrates (company_gender, num_cases) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "recyclingrates", "columns": ["company_gender", "num_cases"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"vessel_capacity\")\nval sink = s\"dw.tmp_${dsNodash}\"\ndf.write.saveAsTable(sink)\n", "labels": {"reads": [{"table": "vessel_capacity", "columns": null}], "writes": []}, "meta": {"template_id": "scala-dynamic-interp", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 403;\nSQL\n", "labels": {"reads": [{"table": "ai_safety", "columns": ["missionid", "funding_amount"]}, {"table": "underwater_cables", "columns": ["kids", "advisoryid", "indigenous", "treatment_date"]}], "writes": [{"table": "ocean_pollution", "columns": ["kids", "advisoryid", "indigenous", "treatment_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO eu_data_usage (donorgender, performancedate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "eu_data_usage", "columns": ["donorgender", "performancedate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO artist SELECT lastclaimdate, sentence_id FROM ocean_acidification WHERE lastclaimdate > 24\"\n", "labels": {"reads": [{"table": "ocean_acidification", "columns": ["lastclaimdate", "sentence_id"]}], "writes": [{"table": "artist", "columns": ["lastclaimdate", "sentence_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws.shipments_daily SELECT shares, project_type, gamename, is_ev FROM investments WHERE shares > 359\"], check=True)\n", "labels": {"reads": [{"table": "investments", "columns": ["shares", "project_type", "gamename", "is_ev"]}], "writes": [{"table": "dws.shipments_daily", "columns": ["shares", "project_type", "gamename", "is_ev"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "# model gene depends on dws.dws_inventory_di\ndbt build -s gene --vars '{\"src\":\"dws.dws_inventory_di\"}'\n", "labels": {"reads": [{"table": "dws.dws_inventory_di", "columns": null}], "writes": [{"table": "gene", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "Dataset df = spark.table(\"ref_incident_type\");\nString sink = \"dw.tmp_\" + dsNodash;\ndf.write().saveAsTable(sink);\n", "labels": {"reads": [{"table": "ref_incident_type", "columns": null}], "writes": []}, "meta": {"template_id": "java-dynamic-concat", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM singer_in_concert\", conn)\ndf.to_sql(\"disaster_zones\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": [{"table": "disaster_zones", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT organic_matter, fuelconsumed FROM royal_family LIMIT 284\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "royal_family", "columns": ["organic_matter", "fuelconsumed"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO party SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.spill_name > 121).all()\n# src table: foodsafetyrecords\nengine.execute(\"INSERT INTO stateinfrastructure SELECT * FROM foodsafetyrecords\")\n", "labels": {"reads": [{"table": "foodsafetyrecords", "columns": null}], "writes": [{"table": "stateinfrastructure", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO catalog_structure SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "// legacy: INSERT INTO public.crime_types SELECT * FROM legacy\nspark.sql(\"INSERT INTO organization_contact_individuals SELECT participated_in_open_pedagogy, country_name, component_name, customer_first_name FROM bus_routes WHERE participated_in_open_pedagogy > 428\")\n", "labels": {"reads": [{"table": "bus_routes", "columns": ["participated_in_open_pedagogy", "country_name", "component_name", "customer_first_name"]}], "writes": [{"table": "organization_contact_individuals", "columns": ["participated_in_open_pedagogy", "country_name", "component_name", "customer_first_name"]}]}, "meta": {"template_id": "scala-commented-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "if not rows:\n logger.warning('empty result')\nspark.sql(\"INSERT INTO vaccine_administered SELECT inclusivehousing, energy_efficiency_kwh_m2_year FROM satellite_deployment WHERE inclusivehousing > 172\")\n", "labels": {"reads": [{"table": "satellite_deployment", "columns": ["inclusivehousing", "energy_efficiency_kwh_m2_year"]}], "writes": [{"table": "vaccine_administered", "columns": ["inclusivehousing", "energy_efficiency_kwh_m2_year"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"voting_data\")\nsrc.write.insertInto(\"ads.ads_cart_item_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "voting_data", "columns": null}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO smart_contracts_transactions SELECT amountdonated, ironid, room FROM waste_types WHERE amountdonated > 71\")\n", "labels": {"reads": [{"table": "waste_types", "columns": ["amountdonated", "ironid", "room"]}], "writes": [{"table": "smart_contracts_transactions", "columns": ["amountdonated", "ironid", "room"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "cur.execute(\"SELECT service_name, catalog_level_number FROM apartments LIMIT 382\")\nrows = cur.fetchall()\nif not rows:\n logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "apartments", "columns": ["service_name", "catalog_level_number"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "logger.info(s\"job start ${java.time.LocalDate.now}\")\nimport org.apache.spark.sql.functions._\nval threshold = args.headOption.map(_.toDouble).getOrElse(0.5)\ntableEnv.executeSql(\"INSERT INTO fish_farms SELECT host_id, impact_id, co2_emission, position FROM fields_production WHERE host_id > 162\")\n", "labels": {"reads": [{"table": "fields_production", "columns": ["host_id", "impact_id", "co2_emission", "position"]}], "writes": [{"table": "fish_farms", "columns": ["host_id", "impact_id", "co2_emission", "position"]}]}, "meta": {"template_id": "scala-flink-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql \"$DB_URL\" < 44;\nSQL\n", "labels": {"reads": [{"table": "diversity", "columns": ["investment_round", "sea"]}, {"table": "labor_costs", "columns": ["athlete_name", "workshop_id", "era", "class_senator_vote"]}], "writes": [{"table": "climate_adaptation_projects", "columns": ["athlete_name", "workshop_id", "era", "class_senator_vote"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "val df = spark.table(\"spacecraft_components\")\ndf.filter($\"status\" === \"OK\").write.mode(\"overwrite\").saveAsTable(\"waterconservationbudget\")\n", "labels": {"reads": [{"table": "spacecraft_components", "columns": null}], "writes": [{"table": "waterconservationbudget", "columns": null}]}, "meta": {"template_id": "scala-save-table", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "JAVA", "content": "log.info(\"job start {}\", LocalDate.now());\nspark.sql(\"INSERT INTO restaurants SELECT movie, hours_developed, noise_level FROM ads.campaigns_di WHERE movie > 110\");\n", "labels": {"reads": [{"table": "ads.campaigns_di", "columns": ["movie", "hours_developed", "noise_level"]}], "writes": [{"table": "restaurants", "columns": ["movie", "hours_developed", "noise_level"]}]}, "meta": {"template_id": "java-spark-sql", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO low_value_contracts SELECT carriername, vr_platform FROM elements_price WHERE carriername > 400\"\n", "labels": {"reads": [{"table": "elements_price", "columns": ["carriername", "vr_platform"]}], "writes": [{"table": "low_value_contracts", "columns": ["carriername", "vr_platform"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SCALA", "content": "spark.table(\"spacex_missions\").where(\"dt = current_date()\").writeTo(\"trip\").append()\n", "labels": {"reads": [{"table": "spacex_missions", "columns": null}], "writes": [{"table": "trip", "columns": null}]}, "meta": {"template_id": "scala-write-to", "rule_covered": true, "form_family": "jvm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO school_roster (vol_id, institution_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "school_roster", "columns": ["vol_id", "institution_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi.bi_inventory_delta (wildlife_type_id, contract_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_inventory_delta", "columns": ["wildlife_type_id", "contract_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"education_union\")\nsrc.write.insertInto(\"weather\", overwrite=True)\n", "labels": {"reads": [{"table": "education_union", "columns": null}], "writes": [{"table": "weather", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "src = spark.read.table(\"aquaticfarm\")\nsrc.write.insertInto(\"dwd.dwd_campaigns\", overwrite=True)\n", "labels": {"reads": [{"table": "aquaticfarm", "columns": null}], "writes": [{"table": "dwd.dwd_campaigns", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table ods.ods_users_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO billstatus (siteid, city_area) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "billstatus", "columns": ["siteid", "city_area"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "spark.sql(\"SELECT participation_date, firstname FROM safetytesting LIMIT 368\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO employeedemographics SELECT id, production_value, satellite FROM renewabletypes WHERE id > 293\")\n", "labels": {"reads": [{"table": "safetytesting", "columns": ["participation_date", "firstname"]}, {"table": "renewabletypes", "columns": ["id", "production_value", "satellite"]}], "writes": [{"table": "employeedemographics", "columns": ["id", "production_value", "satellite"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO programoutcomes SELECT a.veteran_unemployment_rate, b.school_id FROM recycling_stats a JOIN org_volunteer b ON a.billing_country = b.billing_country\"\n", "labels": {"reads": [{"table": "recycling_stats", "columns": null}, {"table": "org_volunteer", "columns": null}], "writes": [{"table": "programoutcomes", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}} +{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO open_pedagogy_exam SELECT * FROM legacy\ncur.execute(\"SELECT material_id, gender_group FROM vendorfabrics LIMIT 428\")\n", "labels": {"reads": [{"table": "vendorfabrics", "columns": ["material_id", "gender_group"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}