~/PyPro/data-trace-agent main
base ❯ python setup_warehouse.py
[ok] warehouse: /Users/xlisp/PyPro/data-trace-agent/data/warehouse.db
[ok] sources: /Users/xlisp/PyPro/data-trace-agent/data/sources
[ok] today files: {'s3': '/Users/xlisp/PyPro/data-trace-agent/data/sources/s3_clickstream/2026-04-26.json', 'log': '/Users/xlisp/PyPro/data-trace-agent/data/sources/app_logs/2026-04-26.log', 'a': '/Users/xlisp/PyPro/data-trace-agent/data/sources/customer_a/2026-04-26.csv', 'b': '/Users/xlisp/PyPro/data-trace-agent/data/sources/customer_b/2026-04-26.csv'}
[ok] last 5 days of daily_metrics:
('2026-04-26', 1500, 479, 56, 6808.62)
('2026-04-25', 1497, 480, 136, 18867.23)
('2026-04-24', 1455, 480, 122, 17926.15)
('2026-04-23', 1543, 479, 131, 19078.55)
('2026-04-22', 1431, 478, 130, 18410.17)
[ok] customer_b: file=80 rows -> DB=5 rows (bug: dropped non-USD)
[ok] customer_a: file=51 rows -> DB=51 rows (bug: amount int-truncated)
~/PyPro/data-trace-agent main*
base ❯ python ./trace_agent.py
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[ok] loaded 18 MCP tools: ['connect_database', 'execute_query', 'describe_table', 'add_field_lineage', 'trace_field_lineage', 'analyze_query_lineage', 'generate_sample_data', 'import_csv', 'export_table_to_csv', 'read_file', 'write_file', 'append_file', 'list_directory', 'get_file_info', 'execute_command', 'get_current_directory', 'create_directory', 'search_files_ag']
/Users/xlisp/PyPro/data-trace-agent/./trace_agent.py:138: LangGraphDeprecatedSinceV10: create_react_agent has been moved to `langchain.agents`. Please update your import to `from langchain.agents import create_agent`. Deprecated in LangGraph V1.0 to be removed in V2.0.
agent = create_react_agent(_make_llm(), tools, prompt=SYSTEM_PROMPT)
=== data-trace-agent ready ===
Sample questions you can try:
- Today's total_revenue in daily_metrics looks low compared to last month. By how much, and why?
- Where does daily_metrics.total_events come from upstream? Show me the lineage.
- For today's customer_b_orders_raw rows, do the DB amounts match the upstream source file? If they differ, why?
- For today's customer_a_orders_raw rows, do the DB amounts match the upstream source file exactly? Pick a few primary keys and compare.
Type your question (blank line / Ctrl-D to quit).
you> hi
agent> Hello! I'm your Data Lineage and Data Support agent. I'm here to help you investigate issues in your data warehouse.
I can assist you with:
1. **Anomaly explanations** — If you notice unexpected changes in metrics (e.g., "Why did total_revenue drop today compared to last month?")
2. **Lineage questions** — Tracing where fields come from upstream (e.g., "Where does the active_users field in daily_metrics come from?")
3. **ETL discrepancy investigations** — Checking if database values match the source files to identify potential loader bugs (e.g., "Does the order data in the DB match what's in the source file?")
I have access to:
- Your warehouse database at `/Users/xlisp/PyPro/data-trace-agent/data/warehouse.db`
- Source files in `/Users/xlisp/PyPro/data-trace-agent/data/sources`
- Metadata tables that track field lineage and source registries
Today's date is **2026-04-26**.
What would you like me to investigate?
you> 今天的 total_revenue 比上个月低很多——具体低了多少，根本原因是什么？也请检查一下上游原始源文件。
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agent> ## 🔴 **调查结论：发现严重的 ETL Bug！**
### **数据异常情况**
**今天 (2026-04-26) 的 total_revenue 严重下降：**
- **今天**: 6,808.62 美元
- **过去30天平均**: 18,501.02 美元
- **下降幅度**: **-63.2%** (少了 11,692.40 美元)
- **过去30天范围**: 17,127.01 - 19,812.02 美元
今天的收入是过去30天中**最低的**，远低于正常范围。
---
### **根本原因：`load_customer_b_orders` 加载器有 Bug**
**问题详情：**
1. **源文件 vs 数据库对比 (Customer B):**
- **源文件** (`api://customer-b/orders` → `/Users/xlisp/PyPro/data-trace-agent/data/sources/customer_b/2026-04-26.csv`):
- 总行数: **81 行** (包含 header，实际订单 80 条)
- USD 订单: **5 条**
- EUR 订单: **75 条**
- **数据库** (`customer_b_orders_raw`):
- 加载的订单: **仅 5 条**
- 全部是 USD 订单 (order_id 8000000-8000004)
2. **Customer A 正常:**
- 源文件: 52 行 (51 条订单 + header)
- 数据库: 51 条订单 ✅
- 金额: 5,764.00 美元 ✅
3. **收入构成对比:**
2026-04-26:
- Customer A: 5,764.00 (51 订单)
- Customer B: 1,044.62 (仅 5 订单) ❌
- 合计: 6,808.62
2026-04-25 (正常日):
- Customer A: 6,170.67 (52 订单)
- Customer B: 12,696.56 (84 订单) ✅
- 合计: 18,867.23
---
### **Bug 诊断**
**`load_customer_b_orders` 加载器只加载了 USD 货币的订单，完全跳过了所有 EUR 订单！**
- 源文件中有 75 条 EUR 订单（占 93.75%）
- 这些 EUR 订单在数据库中**完全缺失**
- 导致 Customer B 的收入从正常的 ~12,000 美元暴跌到 1,044.62 美元
**可能的 Bug 原因：**
- 加载器可能有货币过滤逻辑，错误地只处理 USD
- 或者 EUR 行的解析失败被静默忽略
- 需要检查 `load_customer_b_orders` 的代码逻辑
---
### **影响范围**
- **直接影响**: `customer_b_orders_raw` 表缺失 75 条订单
- **下游影响**: `daily_metrics.total_revenue` 和 `total_orders` 字段严重低估
- **数据完整性**: 今天的报表数据**不可信**
---
### **建议修复步骤**
1. **立即修复** `load_customer_b_orders` 加载器，确保正确处理 EUR 货币
2. **重新加载** 2026-04-26 的 customer_b 数据
3. **重新运行** `agg_orders_daily` ETL job 以更新 `daily_metrics`
4. **回溯检查** 之前的日期是否也有类似问题
you> daily_metrics.total_events 在上游来源是哪里？
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agent> ## **`daily_metrics.total_events` 的上游来源**
根据 `_field_lineage` 表的记录：
### **数据血缘**
**目标字段**: `daily_metrics.total_events`
**上游来源** (2个源表):
1. **`s3_clickstream_raw.event_id`**
- 数据源: `s3://events/clickstream`
- 文件位置: `/Users/xlisp/PyPro/data-trace-agent/data/sources/s3_clickstream`
- 加载器: `load_s3_clickstream`
- 格式: NDJSON (每行一个事件: `{event_id, user_id, page, ts}`)
2. **`app_logs_raw.log_id`**
- 数据源: `fluentd://app-logs`
- 文件位置: `/Users/xlisp/PyPro/data-trace-agent/data/sources/app_logs`
- 加载器: `load_app_logs`
- 格式: 纯文本日志 (`<ts> <level> <user_id|-> <action>` 每行一条)
### **转换逻辑**
- **ETL Job**: `agg_events_daily`
- **计算方式**: `COUNT(*)` 对两个原始事件流进行计数，按 `event_date` 分组
- **含义**: 统计每天来自 S3 点击流和应用日志的**总事件数**
---
**总结**: `total_events` 是 **S3 点击流事件** 和 **应用日志事件** 的总和。
you>
~/PyPro/data-trace-agent main 15m 54s
base ❯ python ./trace_agent.py
[04/27/26 08:16:50] INFO Processing request of type ListToolsRequest server.py:713
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[ok] loaded 18 MCP tools: ['connect_database', 'execute_query', 'describe_table', 'add_field_lineage', 'trace_field_lineage', 'analyze_query_lineage', 'generate_sample_data', 'import_csv', 'export_table_to_csv', 'read_file', 'write_file', 'append_file', 'list_directory', 'get_file_info', 'execute_command', 'get_current_directory', 'create_directory', 'search_files_ag']
/Users/xlisp/PyPro/data-trace-agent/./trace_agent.py:139: LangGraphDeprecatedSinceV10: create_react_agent has been moved to `langchain.agents`. Please update your import to `from langchain.agents import create_agent`. Deprecated in LangGraph V1.0 to be removed in V2.0.
agent = create_react_agent(_make_llm(), tools, prompt=SYSTEM_PROMPT)
=== data-trace-agent ready ===
Sample questions you can try:
- Today's total_revenue in daily_metrics looks low compared to last month. By how much, and why?
- Where does daily_metrics.total_events come from upstream? Show me the lineage.
- For today's customer_b_orders_raw rows, do the DB amounts match the upstream source file? If they differ, why?
- For today's customer_a_orders_raw rows, do the DB amounts match the upstream source file exactly? Pick a few primary keys and compare.
Type your question (blank line / Ctrl-D to quit).
you> 对于今天 customer_a_orders_raw 中的行，数据库里的金额和上游源文件里的完全一致吗？如果不一致，请指出是哪个加载器的问题，并解释这个 bug。
[tool-call] describe_table
table_name: customer_a_orders_raw
args: {"db_path": "/Users/xlisp/PyPro/data-trace-agent/data/warehouse.db"}
[tool-call] execute_query
query: SELECT * FROM _source_registry WHERE source_table = 'customer_a_orders_raw'
args: {"db_path": "/Users/xlisp/PyPro/data-trace-agent/data/warehouse.db"}
[tool-call] execute_query
query:
SELECT * FROM customer_a_orders_raw WHERE date(order_date) = '2026-04-26' ORDER BY order_id
args: {"db_path": "/Users/xlisp/PyPro/data-trace-agent/data/warehouse.db"}
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[tool-result:execute_query] [{'type': 'text', 'text': 'Error executing query: no such column: order_date', 'id': 'lc_83a89b9d-cd98-4a00-8691-4c51dbbb2974'}]
[tool-result:describe_table] [{'type': 'text', 'text': 'Table: customer_a_orders_raw\nRow count: 1553\nColumns:\n[\n {\n "name": "order_id",\n "type": "INTEGER",\n "nullable": true,\n "default": null,\n "primary_key": true\n },\n {\n "name": "user_id",\n "type": "INTEGER",\n "nullable": false,\n "default": null,\n "primary_key": false\n },\n {\n "name": "amount",\n "type": "REAL",\n "nullable": false,\n "default": null,\n "primary_key": false\n },\n {\n "name": "ts",\n "type": "TEXT",\n "nullable": false,\n "default": null,\n "primary_key": false\n },\n {\n "name": "source",\n "type": "TEXT",\n "nullable": false,\n "default": "\'sftp://customer-a/orders\'",\n "primary_key": false\n }\n]', 'id': 'lc_c97d1793-f042-45e9-85e2-3324de31a7b7... [+3 chars truncated]
[tool-result:execute_query] [{'type': 'text', 'text': 'Query executed successfully.\nResults:\n[\n {\n "source_table": "customer_a_orders_raw",\n "source_uri": "sftp://customer-a/orders",\n "file_dir": "/Users/xlisp/PyPro/data-trace-agent/data/sources/customer_a",\n "loader": "load_customer_a_orders",\n "schema_note": "CSV with header: order_id,user_id,amount,currency,ts. All amounts USD with 2 decimals."\n }\n]', 'id': 'lc_03c1f558-c9ee-4370-9b4e-f5b2a2192a85'}]
[tool-call] execute_query
query:
SELECT * FROM customer_a_orders_raw WHERE date(ts) = '2026-04-26' ORDER BY order_id
args: {"db_path": "/Users/xlisp/PyPro/data-trace-agent/data/warehouse.db"}
[tool-call] read_file
file_path: /Users/xlisp/PyPro/data-trace-agent/data/sources/customer_a/2026-04-26.csv
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[tool-result:read_file] [{'type': 'text', 'text': 'File: /Users/xlisp/PyPro/data-trace-agent/data/sources/customer_a/2026-04-26.csv\nSize: 2195 characters\n\norder_id,user_id,amount,currency,ts\n7000000,309,119.06,USD,2026-04-26T07:17:28\n7000001,29,100.88,USD,2026-04-26T00:13:59\n7000002,396,148.09,USD,2026-04-26T01:02:03\n7000003,443,121.74,USD,2026-04-26T00:44:11\n7000004,123,94.21,USD,2026-04-26T06:08:55\n7000005,104,105.57,USD,2026-04-26T08:10:27\n7000006,50,66.99,USD,2026-04-26T23:53:02\n7000007,66,154.72,USD,2026-04-26T22:05:17\n7000008,53,83.47,USD,2026-04-26T00:23:02\n7000009,359,181.13,USD,2026-04-26T17:35:49\n7000010,267,109.67,USD,2026-04-26T23:22:04\n7000011,456,72.76,USD,2026-04-26T22:44:31\n7000012,294,114.74,USD,2026-04-26T18:42:04\n7000013,356,41.39,USD,2026-04-26T13:36:09\n7000014,355,51.96,USD,... [+1633 chars truncated]
[tool-result:execute_query] [{'type': 'text', 'text': 'Query executed successfully.\nResults:\n[\n {\n "order_id": 7000000,\n "user_id": 309,\n "amount": 119.0,\n "ts": "2026-04-26T07:17:28",\n "source": "sftp://customer-a/orders"\n },\n {\n "order_id": 7000001,\n "user_id": 29,\n "amount": 100.0,\n "ts": "2026-04-26T00:13:59",\n "source": "sftp://customer-a/orders"\n },\n {\n "order_id": 7000002,\n "user_id": 396,\n "amount": 148.0,\n "ts": "2026-04-26T01:02:03",\n "source": "sftp://customer-a/orders"\n },\n {\n "order_id": 7000003,\n "user_id": 443,\n "amount": 121.0,\n "ts": "2026-04-26T00:44:11",\n "source": "sftp://customer-a/orders"\n },\n {\n "order_id": 7000004,\n "user_id": 123,\n "amount": 94.0,\n "ts": "2026-04-26T06:08:55",\n... [+7244 chars truncated]
agent> 完美！现在我可以对比数据库和源文件中的金额了。让我详细比较几个订单：
## 发现的问题：**金额数据不一致！**
数据库中的金额与上游源文件**完全不一致**。所有金额都被错误地截断为整数，丢失了小数部分。
### 具体对比（前10个订单）：
| order_id | 源文件金额 | 数据库金额 | 差异 |
|----------|-----------|-----------|------|
| 7000000 | **119.06** | 119.0 | -0.06 |
| 7000001 | **100.88** | 100.0 | -0.88 |
| 7000002 | **148.09** | 148.0 | -0.09 |
| 7000003 | **121.74** | 121.0 | -0.74 |
| 7000004 | **94.21** | 94.0 | -0.21 |
| 7000005 | **105.57** | 105.0 | -0.57 |
| 7000006 | **66.99** | 66.0 | -0.99 |
| 7000007 | **154.72** | 154.0 | -0.72 |
| 7000008 | **83.47** | 83.0 | -0.47 |
| 7000009 | **181.13** | 181.0 | -0.13 |
### 问题根源：
**加载器：** `load_customer_a_orders`
**源 URI：** `sftp://customer-a/orders`
### Bug 解释：
`load_customer_a_orders` 加载器在解析 CSV 文件时，**错误地将 amount 字段作为整数处理**，而不是浮点数。这导致所有金额的小数部分被截断。
根据 `_source_registry` 的 schema_note，源文件明确说明："All amounts USD with 2 decimals"（所有金额都是美元，带2位小数）。但加载器显然没有正确处理这个格式，可能使用了 `int()` 而不是 `float()` 来解析 amount 列。
### 影响：
- 今天（2026-04-26）的51个订单全部受影响
- 每个订单损失 0.01 到 0.99 美元不等
- 这会导致下游的 `daily_metrics.total_revenue` 统计不准确，系统性地低估了实际收入
**建议修复：** 修改 `load_customer_a_orders` 加载器，确保将 CSV 中的 amount 字段解析为 `float` 类型，而不是 `int` 类型。
you>
~/PyPro/data-trace-agent main 44s
base ❯