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在前后端开发联调前 的提交20260223

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- [P20260221-115116] 2026-02-21 11:51:16 +0800
- summary: CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary: --- ## TASK 1: Modify "D…
- prompt:
```text
CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary:
---
## TASK 1: Modify "Data Flow Structure Analysis" Hook — Whitelist Refactoring
**STATUS**: in-progress
**USER QUERIES**: 1 (the main request)
**DETAILS**:
The user requested three changes to the existing `dataflow-analyze` hook and its underlying `gen_dataflow_report.py`:
1. **Add to whitelist**: DWD 维表 SCD2 字段 (`valid_from`, `valid_to`, `is_current`, `etl_loaded_at`, `etl_batch_id`) and API `siteProfile` 嵌套信息
2. **Refactor whitelist logic**: All whitelist fields must still be checked (not skipped). The term "跳过" (skip) must never be used — only "白名单" (whitelist). Whitelist fields are folded/collapsed in the report (not shown in detailed tables), with a note explaining the whitelist reason.
3. **Report sections affected**: Section 1.1 (API↔ODS↔DWD field diff) and Section 3 (per-table details)
**What was implemented**:
- Added global whitelist constants and helper functions at top of `gen_dataflow_report.py`: `WHITELIST_ETL_META_COLS`, `WHITELIST_DWD_SCD2_COLS`, `WHITELIST_API_NESTED_PREFIXES`, plus `is_whitelist_etl_meta()`, `is_whitelist_scd2()`, `is_whitelist_api_nested()`, `whitelist_reason()`
- Refactored `_write_field_diff_report()` to separate whitelist items from non-whitelist items in all 4 categories (API→ODS unmapped, ODS no JSON source, ODS→DWD unmapped, DWD no ODS source)
- Added `_write_whitelist_summary()` helper for collapsed whitelist display
- Updated `_write_dwd_section()` to use `is_whitelist_scd2()` instead of hardcoded tuple
- Updated `_write_api_section()` to use `is_whitelist_api_nested()` instead of hardcoded `siteProfile.` check
- Removed all uses of old `etl_meta_cols` local variable
- Removed all instances of "跳过" from the report generator
- Updated hook file to v4.0.0 with new prompt describing whitelist rules
**What still needs verification**:
- The `gen_dataflow_report.py` changes compile clean (verified via `py_compile` and `getDiagnostics`)
- But the report has NOT been regenerated/tested end-to-end yet
- The Section 3 per-table details (`_write_api_section`, `_write_ods_section`, `_write_dwd_section`) still show ALL fields in their tables (including whitelist fields). The user's requirement says whitelist fields should also be folded in Section 3. This part may need additional work — currently only Section 1.1 diff details fold whitelist fields.
**NEXT STEPS**:
- Verify whether Section 3 per-table detail tables also need whitelist folding (user said "3. 逐表详情下的每项，白名单字段不放在详细的表格中"). If so, `_write_api_section`, `_write_ods_section`, and `_write_dwd_section` need modification to exclude whitelist fields from their main tables and add a summary note instead.
- Run the full pipeline to test: `python scripts/ops/analyze_dataflow.py` then `python scripts/ops/gen_dataflow_report.py`
**FILEPATHS**:
- `scripts/ops/gen_dataflow_report.py` (modified)
- `.kiro/hooks/dataflow-analyze.kiro.hook` (modified, v4.0.0)
---
## TASK 2: Create New Hook "ETL Data Consistency Check"
**STATUS**: in-progress
**USER QUERIES**: 1 (the main request)
**DETAILS**:
The user requested a new hook that:
1. Finds the last successful ETL task from logs
2. Uses black-box approach to produce a sampling report similar to `field_level_report_20260220_233443.md` but more detailed
3. Compares actual data across API→ODS→DWD→DWS/INDEX for every table and field, reporting data differences
**What was implemented**:
- Created `.kiro/hooks/etl-data-consistency.kiro.hook` (v1.0.0, userTriggered)
- Created `scripts/ops/etl_consistency_check.py` with full implementation:
- `find_latest_log()` / `parse_etl_log()` — find and parse latest ETL log
- `load_api_json_records()` — read API JSON from FETCH_ROOT
- `check_api_vs_ods()` — field-level + value-level comparison (API JSON vs ODS DB)
- `check_ods_vs_dwd()` — column comparison + value sampling (ODS vs DWD)
- `check_dwd_vs_dws()` — DWS table overview + numeric column sanity checks
- `generate_report()` — full Markdown report with 5 sections
- `main()` — orchestration entry point
- Script uses `PG_DSN` (readonly), `FETCH_ROOT`, `LOG_ROOT`, `ETL_REPORT_ROOT` from `.env`
- Output: `ETL_REPORT_ROOT/consistency_check_<timestamp>.md`
- Both files pass `py_compile` and `getDiagnostics` checks
**What still needs verification/improvement**:
- Script has NOT been run yet — needs end-to-end testing
- The `COUNT(DISTINCT id)` query uses `cur.fetchone()["cnt"]` but the column alias from `COUNT(DISTINCT id)` is `count` not `cnt` — this will cause a KeyError at runtime. Need to fix the SQL alias or dict key.
- The user wanted the report to be "more detailed than field_level_report" — current implementation does value sampling but doesn't include per-field statistics (NULL rate, min/max, distinct count) like `field_level_report.py` does. May need enhancement.
- DWS check is basic (row counts + numeric sanity) — doesn't do actual DWD→DWS aggregation verification
**NEXT STEPS**:
- Fix the `COUNT(DISTINCT id)` SQL alias bug in `check_api_vs_ods()` and `check_ods_vs_dwd()` — change `cur.fetchone()["cnt"]` to `cur.fetchone()["count"]` or add `AS cnt` alias
- Run `uv run python scripts/ops/etl_consistency_check.py` to test end-to-end
- Consider enhancing DWS verification to compare aggregated DWD values against DWS values
- Consider adding per-field statistics similar to `field_level_report.py`
**FILEPATHS**:
- `scripts/ops/etl_consistency_check.py` (new)
- `.kiro/hooks/etl-data-consistency.kiro.hook` (new)
---
## USER CORRECTIONS AND INSTRUCTIONS:
- Language: All explanatory text must be in simplified Chinese (简体中文)
- Never use "跳过" (skip) — only use "白名单" (whitelist)
- Whitelist fields must still be checked/counted — they are only folded in report display
- Output paths must come from `.env` environment variables via `_env_paths.get_output_path()` — never hardcode
- Scripts in `scripts/ops/` use `_env_paths.py` for path resolution
- Database connections use `PG_DSN` from `.env` (readonly mode for reports)
- The background context document is at `export/ETL-Connectors/feiqiu/REPORTS/context_handoff_task2.md`
- Existing mapping tables (`ODS_TASK_TO_TABLE`, `DWD_TO_ODS`) are defined in `scripts/ops/blackbox_test_report.py` and were replicated in the new script
- The project uses `uv run python` to execute scripts
- `scripts/ops/` scripts must `load_dotenv` from root `.env` and raise errors on missing env vars
## Files to read
- `scripts/ops/gen_dataflow_report.py` (modified — whitelist refactoring, verify Section 3 folding requirement)
- `scripts/ops/etl_consistency_check.py` (new — has SQL alias bug to fix, needs testing)
- `.kiro/hooks/dataflow-analyze.kiro.hook` (modified hook definition)
- `.kiro/hooks/etl-data-consistency.kiro.hook` (new hook definition)
- `export/ETL-Connectors/feiqiu/REPORTS/context_handoff_task2.md` (background context)
- `scripts/ops/_env_paths.py` (shared path utility)
- `scripts/ops/blackbox_test_report.py` (reference for mapping tables and report patterns)
- `scripts/ops/field_level_report.py` (reference for the "more detailed" report the user wants)
USER QUERIES(most recent first):
1. 这是背景信息：export\ETL-Connectors\feiqiu\REPORTS\context_handoff_task2.md修改一个HOOKS：Data Flow Structure Analysis，要求：- 白名单/跳过增加：DWD的维表SCD2字段。API中siteProfile嵌套信。- 重构所有白名单和被跳过字段的处理逻辑：这些字段都要正常检查，检查时不得跳过！之前用“跳过”一词不严谨，进行修改，之后不使用这个词，只用“白名单”。白名单的作用为：在报告中，1.1 API↔ODS↔DWD 字段对比差异章节下的每项，及3. 逐表详情下的每项，白名单字段不放在详细的表格中，并说明因白名单或其他具体原因，不展开详细信息。增加一个HOOKS，名称来定，其工作方式类似Data Flow Structure Analysis，要求：1 获取上一个执行成功的ETL任务。2 黑盒手段，出具类似export\ETL-Connectors\feiqiu\REPORTS\field_level_report_20260220_233443.md的采样报告。但要比这个详细见下文。3 对本次任务中，API - ODS - DWD - DWS/INDEX 的每个表的每个字段进行检查，进行数据上下流的各表各字段对比 （实际数据库中及实际JSON中比对），看是否在本次任务中，有对不上的数据的情况。并对数据差异进行报告列举。
2. Output:
Command executed successfully with no output.
Exit Code: 0
3. Output:
Command executed successfully with no output.
Exit Code: 0
4. 这是背景信息：export\ETL-Connectors\feiqiu\REPORTS\context_handoff_task2.md修改一个HOOKS：Data Flow Structure Analysis，要求：- 白名单/跳过增加：DWD的维表SCD2字段。API中siteProfile嵌套信。- 重构所有白名单和被跳过字段的处理逻辑：这些字段都要正常检查，检查时不得跳过！之前用“跳过”一词不严谨，进行修改，之后不使用这个词，只用“白名单”。白名单的作用为：在报告中，1.1 API↔ODS↔DWD 字段对比差异章节下的每项，及3. 逐表详情下的每项，白名单字段不放在详细的表格中，并说明因白名单或其他具体原因，不展开详细信息。增加一个HOOKS，名称来定，其工作方式类似Data Flow Structure Analysis，要求：1 获取上一个执行成功的ETL任务。2 黑盒手段，出具类似export\ETL-Connectors\feiqiu\REPORTS\field_level_report_20260220_233443.md的采样报告。但要比这个详细见下文。3 对本次任务中，API - ODS - DWD - DWS/INDEX 的每个表的每个字段进行检查，进行数据上下流的各表各字段对比 （实际数据库中及实际JSON中比对），看是否在本次任务中，有对不上的数据的情况。并对数据差异进行报告列举。
---
METADATA:
The previous conversation had 2 messages.
INSTRUCTIONS:
Continue working until the user query has been fully addressed. Do not ask for clarification - proceed with the work based on the context provided.
IMPORTANT: you need to read from the files to Read section
```