Datasets:

dicemy
/

DataClawEval

id: offline-compute_PySpark_pyspark_009
name: Anti-Spam H5 Work Order Log Daily Detail Sync
category: offline-compute/PySpark
timeout_seconds: 900
modality: pure-text
engine: pyspark

Prompt

I need you to generate a PySpark script that synchronizes the "Anti-Spam H5 Work Order Log" from ODS to the DWD detail table with full-field passthrough, and adds a run date column.

Business Background and Objective: The anti-spam H5 work order service writes each appeal/report operation log to the raw log_80001099 table (48 business fields + databus_imp_date partition). The downstream DWD wants to transport the full-field detail of the current day's partition every day, and additionally add a column imp_date (run date, YYYYMMDD) for convenient subsequent filtering by run date. The task has no aggregation and no joins — pure detail transport + one derived column.

Input Table:

internal_platform_db.caseR18_ods_log_80001099_v3

Data Range and Filter Conditions (business description):

The original task in production reads from the full table (without imp_date filter); the sandbox v3 table is loaded by ds for the corresponding date, so the SELECT has no WHERE clause and reads the full table.
The run date ds is fixed as '20260513' and will be written as the new column imp_date.

Table Join Relationships: (No joins, single-table passthrough.)

Aggregation and Computation Rules (must be reflected in the SQL):

Derived column imp_date = '20260513' (run date constant), placed as the first column in the SELECT.
The following 49 columns are passed through as-is: databus_imp_date + 48 business fields, in the same order as the DDL.
Use INSERT OVERWRITE TABLE + explicit column name list (50 columns) to avoid column order misalignment.

Output Requirements:

Target table: internal_platform_db.caseR18_dwd_log_80001099_daily_v3
Output table schema (50 columns, in this order, with the following semantics; the first 2 columns are run/original partition date, the remaining 48 are business fields):
1. imp_date STRING: run date YYYYMMDD = '20260513'
2. databus_imp_date STRING: original partition date (passed through from input)
3. logid BIGINT, 4. svrtime STRING, 5. svrip STRING, 6. module STRING
4. form_id STRING, 8. form_type BIGINT, 9. action BIGINT, 10. retcode BIGINT
5. vid BIGINT, 12. corpid BIGINT, 13. gid BIGINT
6. appeal_kind BIGINT (appeal problem type), 15. report_kind BIGINT (report problem type), 16. fraud_kind BIGINT (fraud type), 17. loss_amount BIGINT (loss amount)
7. suspect_vid BIGINT, 19. suspect_corpid BIGINT
8. order_id STRING (work order number), 21. order_result BIGINT, 22. order_source BIGINT, 23. order_action BIGINT
9. match_rule STRING, 25. roomid BIGINT (problematic group number), 26. create_vid BIGINT, 27. openid STRING
10. isfromwx BIGINT (interception party ww/wx), 29. spamtype STRING, 30. auto_finish_order_rule STRING
11. action_time BIGINT (action occurrence time), 32. industry_name STRING, 33. second_industry_name STRING, 34. is_ka BIGINT
12. create_time STRING, 36. urgent_time STRING, 37. result_time STRING, 38. reopen_time BIGINT
13. block_uin BIGINT, 40. block_user_id_type BIGINT, 41. version STRING (v1/v2 differentiation)
14. h5_source BIGINT (0: frontline customer service, 1: user self-service), 43. h5_platform BIGINT (1: BizComm, 2: PlatformW), 44. template_type BIGINT
15. current_owner STRING, 46. oid BIGINT (appeal work order numeric ID), 47. reason STRING (QA closing evidence reason, base64)
16. order_finish_type STRING, 49. reply_content STRING (scripted reply, base64), 50. match_rule_scene_id BIGINT
Write strategy: INSERT OVERWRITE TABLE + explicit 50-column name list.
Sandbox note: The original task is encapsulated in SparkBase (SparkJob class), using the self.load_dw_data().createOrReplaceTempView() + self.save_dw_data() chain; the sandbox version is rewritten as a top-level SparkSession + a single INSERT OVERWRITE TABLE ... SELECT.
If the target table does not exist, first create it using standard Hive format (ORC storage), then write the data.