6.4 KiB
6.4 KiB
- [P20260221-123335] 2026-02-21 12:33:35 +0800
- summary: CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary: --- ## TASK 1: Execute D…
- prompt:
CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary:
---
## TASK 1: Execute Data Flow Structure Analysis (数据流结构分析)
**STATUS**: in-progress
**USER QUERIES**: User's single request - Execute dataflow structure analysis in two phases: (1) data collection via `analyze_dataflow.py`, (2) report generation via `gen_dataflow_report.py`
**DETAILS**:
- User requested a full dataflow structure analysis for the feiqiu (飞球) connector
- Two-phase process: Phase 1 = data collection, Phase 2 = report generation
- The agent confirmed `scripts/ops/analyze_dataflow.py` exists but was cut off before executing anything
- No commands have been run yet - the task is at the very beginning
- The user specified that if historical task artifacts exist, they should be cleared and re-executed
**NEXT STEPS**:
1. Check the output directory status (likely `SYSTEM_ANALYZE_ROOT` from `.env`) for any existing artifacts
2. Run `python scripts/ops/analyze_dataflow.py` from the project root to collect data
3. Verify collection results are on disk: `json_trees/`, `db_schemas/`, `field_mappings/`, `bd_descriptions/`, `collection_manifest.json`
4. Run `python scripts/ops/gen_dataflow_report.py` to generate the Markdown report
5. Verify report contains all required enhanced content (API date range, JSON field counts, field diff report with whitelist folding, business descriptions, anchor links, etc.)
6. Output the file path and key statistics summary
**KEY CONTEXT**:
- Project is a billiard hall (台球门店) data platform monorepo called NeoZQYY
- ETL pipeline: API → ODS → DWD → DWS with PostgreSQL
- Four DB instances: `etl_feiqiu`, `test_etl_feiqiu`, `zqyy_app`, `test_zqyy_app`
- Environment variables control all output paths (see `export-paths.md` steering)
- Output paths come from `.env` - key vars: `SYSTEM_ANALYZE_ROOT`, `FULL_DATAFLOW_DOC_ROOT`
- Scripts must be run with `uv run python` or `python` from project root `C:\NeoZQYY`
- OS is Windows with cmd shell
- Whitelist rules (v4): ETL meta cols, SCD2 cols, siteProfile nested fields - still checked but folded in report
- Only analyzing feiqiu connector currently
**FILEPATHS**:
- `scripts/ops/analyze_dataflow.py` - Phase 1: data collection script
- `scripts/ops/gen_dataflow_report.py` - Phase 2: report generation script (partially loaded, truncated at ~806/889 lines)
- `scripts/ops/field_level_report.py` - Related field-level analysis script
- `scripts/ops/etl_consistency_check.py` - Related consistency check script (partially loaded, truncated at ~811/1011 lines)
- `.env` - Environment variables (not read yet, needed for paths)
- `.env.template` - Template for env vars
- `apps/etl/connectors/feiqiu/docs/architecture/data_flow.md` - Architecture documentation
- `export/SYSTEM/REPORTS/full_dataflow_doc/dataflow_api_ods_dwd.md` - Previous report output (4838 lines, only 408 loaded)
**USER CORRECTIONS AND INSTRUCTIONS**:
- All responses must be in simplified Chinese (简体中文) per `language-zh.md` steering
- Must use `.env` for all output paths - never hardcode (per `export-paths.md`)
- Testing/scripts must load `.env` properly (per `testing-env.md`)
- Prefer Python scripts over PowerShell for complex operations (per `tech.md`)
- `cwd` for ETL scripts should be `apps/etl/connectors/feiqiu/` but these ops scripts run from project root
- DB connections use `PG_DSN` from `.env`
- This is NOT a spec creation task - it's a direct execution task despite the system prompt mentioning spec workflow
**Files to read**:
- `scripts/ops/analyze_dataflow.py`
- `scripts/ops/gen_dataflow_report.py`
- `.env.template`
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:
</source-event>
执行数据流结构分析，按以下步骤完成。若发现已完成或有历史任务痕迹则清空，重新执行：
第一阶段：数据采集
1. 运行 `python scripts/ops/analyze_dataflow.py` 完成数据采集（如需指定日期范围，加 --date-from / --date-to 参数）
2. 确认采集结果已落盘，包括：
- json_trees/（含 samples 多示例值）
- db_schemas/
- field_mappings/（三层映射 + 锚点）
- bd_descriptions/（BD_manual 业务描述）
- collection_manifest.json（含 json_field_count、date_from、date_to）
第二阶段：报告生成
3. 运行 `python scripts/ops/gen_dataflow_report.py` 生成 Markdown 报告
4. 报告包含以下增强内容：
- 报告头含 API 请求日期范围（date_from ~ date_to）和 JSON 数据总量
- 总览表含 API JSON 字段数列
- 1.1 API↔ODS↔DWD 字段对比差异报告（白名单字段折叠汇总，不展开详细表格行）
- 2.3 覆盖率表含业务描述列
- API 源字段表含业务描述列 + 多示例值（枚举值解释）
- ODS 表结构含业务描述列 + 上下游双向映射锚点链接
- DWD 表结构含业务描述列 + ODS 来源锚点链接
5. 输出文件路径和关键统计摘要
白名单规则（v4）：
- ETL 元数据列（source_file, source_endpoint, fetched_at, payload, content_hash）
- DWD 维表 SCD2 管理列（valid_from, valid_to, is_current, etl_loaded_at, etl_batch_id）
- API siteProfile 嵌套对象字段
- 白名单字段仍正常参与检查和统计，仅在报告中折叠显示并注明原因
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
Focus on creating a new spec file or identifying an existing spec to update.
If starting a new spec, create a requirements.md file in the .kiro/specs directory with clear user stories and acceptance criteria.
If working with an existing spec, review the current requirements and suggest improvements if needed.
Do not make direct code changes yet. First establish or review the spec file that will guide our implementation.</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