- [P20260216-200722] 2026-02-16 20:07:22 +0800
  - summary: CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary: --- ## TASK 1: 数据流结构分析 —…
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CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary:

---
## TASK 1: 数据流结构分析 — 完整三阶段执行

**STATUS**: in-progress

**USER QUERIES**: 1 ("执行数据流结构分析，按以下步骤完成：第一阶段数据采集、第二阶段语义分析、第三阶段报告生成")

**DETAILS**:

### 第一阶段：数据采集 — DONE
- Ran `python scripts/ops/analyze_dataflow.py --limit 200` successfully
- 23 tables collected, all succeeded, 3405 total records
- Output to `C:\NeoZQYY\export\dataflow_analysis\` with subdirs: `json_trees/`, `db_schemas/`, `collection_manifest.json`
- DWD tables all returned 0 columns (DWD table names don't match ODS table names — DWD uses dimension/fact table names like `dim_member`, `dim_assistant`, not the ODS raw table names). This is expected behavior.

### 第二阶段：语义分析 — IN PROGRESS (data reading complete, analysis not started)
- **ALL 23 json_trees files have been read** into context — complete field structures for all tables
- **5 ODS schema files read**: `ods_settlement_records.json`, `ods_payment_transactions.json`, `ods_member_profiles.json`, `ods_assistant_accounts_master.json`, `ods_site_tables_master.json` — these contain DDL COMMENT annotations with field descriptions
- **Remaining 18 ODS schema files NOT yet read** — need to read them for complete mapping
- **DWD schema files all empty** (0 columns) due to table name mismatch
- **ETL loaders directory explored**: `apps/etl/pipelines/feiqiu/loaders/` has `ods/generic.py`, `dimensions/`, `facts/`, `base_loader.py`
- **ETL source code NOT yet read** — needed for understanding ODS→DWD mapping logic

### 第三阶段：报告生成 — NOT STARTED
- Need to generate final Markdown report with:
  - Per-table: API source field tables, ODS tables, DWD tables
  - JSON→ODS mapping (matched, payload-only, ignored fields)
  - ODS→DWD mapping (direct, ETL-derived, SCD2 version control columns)
  - Field coverage stats, type distribution, upstream/downstream mapping coverage
- Save to `SYSTEM_ANALYZE_ROOT` (`C:\NeoZQYY\export\dataflow_analysis\`) as `dataflow_YYYY-MM-DD_HHMMSS.md`

### Key Data Summary from collection_manifest.json:
| Table | Records | ODS Cols | DWD Cols |
|-------|---------|----------|----------|
| assistant_accounts_master | 69 | 67 | 0 |
| settlement_records | 200 | 71 | 0 |
| table_fee_transactions | 200 | 47 | 0 |
| assistant_service_records | 200 | 71 | 0 |
| assistant_cancellation_records | 79 | 19 | 0 |
| store_goods_sales_records | 200 | 56 | 0 |
| payment_transactions | 200 | 17 | 0 |
| refund_transactions | 36 | 37 | 0 |
| platform_coupon_redemption_records | 200 | 31 | 0 |
| member_profiles | 200 | 25 | 0 |
| member_stored_value_cards | 200 | 80 | 0 |
| member_balance_changes | 200 | 33 | 0 |
| recharge_settlements | 200 | 71 | 0 |
| group_buy_packages | 18 | 43 | 0 |
| group_buy_redemption_records | 200 | 57 | 0 |
| goods_stock_summary | 173 | 19 | 0 |
| goods_stock_movements | 200 | 24 | 0 |
| site_tables_master | 74 | 31 | 0 |
| stock_goods_category_tree | 9 | 16 | 0 |
| store_goods_master | 173 | 52 | 0 |
| table_fee_discount_records | 200 | 33 | 0 |
| tenant_goods_master | 174 | 37 | 0 |
| settlement_ticket_details | 0 | 43 | 0 |

### ODS Schema Pattern (from read files):
- Each ODS table has ETL metadata columns: `content_hash`, `source_file`, `source_endpoint`, `fetched_at`, `payload` (jsonb)
- ODS columns are lowercase versions of JSON camelCase fields (e.g., `settleList.balanceAmount` → `balanceamount`)
- Many tables store nested objects like `siteProfile` as jsonb or flatten the list-key fields only
- DDL COMMENTs follow pattern: `【说明】...【示例】...【JSON字段】...`

**NEXT STEPS**:
1. Read remaining ODS schema files (18 more) from `C:\NeoZQYY\export\dataflow_analysis\db_schemas\ods_*.json`
2. Read ETL source code for data flow understanding:
   - `apps/etl/pipelines/feiqiu/loaders/ods/generic.py` (ODS loader)
   - `apps/etl/pipelines/feiqiu/loaders/base_loader.py`
   - `apps/etl/pipelines/feiqiu/scd/` directory (SCD2 logic for DWD)
   - `apps/etl/pipelines/feiqiu/tasks/` directory (task definitions)
3. For each of the 23 tables, compute:
   - JSON→ODS field mapping (semantic matching between camelCase JSON paths and lowercase ODS columns)
   - Field purpose inference using DDL COMMENT + JSON samples + ETL code
   - ODS→DWD mapping (requires reading DWD DDL files from `db/etl_feiqiu/schemas/` since runtime query returned empty)
4. Generate per-table statistics: field coverage rate, type distribution, mapping coverage
5. Assemble final Markdown report and save to `C:\NeoZQYY\export\dataflow_analysis\dataflow_2026-02-16_HHMMSS.md`

**FILEPATHS**:
- `scripts/ops/analyze_dataflow.py` — CLI entry point
- `scripts/ops/dataflow_analyzer.py` — core collection module with ODS_SPECS
- `C:\NeoZQYY\export\dataflow_analysis\collection_manifest.json` — collection results
- `C:\NeoZQYY\export\dataflow_analysis\json_trees\*.json` — 23 JSON tree files (all read)
- `C:\NeoZQYY\export\dataflow_analysis\db_schemas\ods_*.json` — 23 ODS schema files (5 read)
- `C:\NeoZQYY\export\dataflow_analysis\db_schemas\dwd_*.json` — 23 DWD schema files (all empty/0 cols)
- `apps/etl/pipelines/feiqiu/loaders/` — ETL loader code
- `apps/etl/pipelines/feiqiu/docs/architecture/data_flow.md` — architecture doc (read)
- `.kiro/specs/dataflow-structure-audit/tasks.md` — spec tasks (all completed)

**USER CORRECTIONS AND INSTRUCTIONS**:
- 当前仅分析飞球（feiqiu）连接器
- 报告使用中文
- Output to `SYSTEM_ANALYZE_ROOT` = `C:\NeoZQYY\export\dataflow_analysis`
- Filename format: `dataflow_YYYY-MM-DD_HHMMSS.md`
- DWD layer uses different table names (dim_member, dim_assistant, fact_* etc.) not the ODS raw table names — need to look at DDL files or ETL code to find the actual DWD table mappings
- The workspace steering rules require: Chinese output, UTF-8, audit for high-risk changes, Python scripts for complex ops
- This is a Kiro Hook-triggered analysis workflow (hook at `.kiro/hooks/dataflow-analyze.kiro.hook`)

## Files to read
- `C:\NeoZQYY\export\dataflow_analysis\collection_manifest.json`
- `apps/etl/pipelines/feiqiu/loaders/ods/generic.py`
- `apps/etl/pipelines/feiqiu/loaders/base_loader.py`
- `apps/etl/pipelines/feiqiu/docs/architecture/data_flow.md`
- `scripts/ops/dataflow_analyzer.py`
- `.kiro/specs/dataflow-structure-audit/tasks.md`

USER QUERIES(most recent first):
1. <source-event>
    The user manually invoked this action
    The user is focued on the following file: No file focused
    The user has the following paths open: \apps\etl\pipelines\feiqiu\config\env_parser.py, \apps\etl\pipelines\feiqiu\config\env_parser.py
</source-event>

执行数据流结构分析，按以下步骤完成：

第一阶段：数据采集
1. 运行 `python scripts/ops/analyze_dataflow.py` 完成数据采集（API JSON 原始数据 + DB 表结构查询）
2. 确认采集结果已落盘到 SYSTEM_ANALYZE_ROOT（或 docs/reports/）目录下的 api_samples/、json_trees/、db_schemas/ 子目录

第二阶段：语义分析
3. 读取采集结果中的 JSON 文件（json_trees/、db_schemas/、collection_manifest.json）
4. 读取 ETL 源码（apps/etl/pipelines/feiqiu/ 下的 loaders/、tasks/、models/、scd/ 等模块）理解数据流转逻辑
5. 为每个字段推断作用说明：优先使用 DDL COMMENT 注释，结合 ETL 源码映射关系和 JSON 数据样本综合判断
6. 计算 JSON → ODS 映射关系：基于字段语义匹配，标注已映射、仅存于 payload、被忽略的字段
7. 计算 ODS → DWD 映射关系：基于 ETL loader/task 源码理解数据流转，标注直接映射、ETL 派生、SCD2 版本控制列

第三阶段：报告生成
8. 编排统计总结：为每个表格生成有业务语义的字段统计（字段覆盖率、类型分布、上下游映射覆盖率）
9. 组装最终 Markdown 报告，包含 API 源字段表格、ODS 表格、DWD 表格及各自的统计总结
10. 将报告保存到 SYSTEM_ANALYZE_ROOT 目录，使用 dataflow_YYYY-MM-DD_HHMMSS.md 格式命名
11. 输出文件路径和关键统计摘要

注意：当前仅分析飞球（feiqiu）连接器。未来新增连接器时，应自动发现并纳入分析范围。
2. <implicit-rules>## Implicit Rules
You are working on the implementation plan. 
Ensure each task is actionable, references specific requirements, and focuses only on coding activities. 
Once approved, inform the user that the spec is complete and they can begin implementing the tasks by opening the tasks.md file.</implicit-rules>
---

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
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