debugging-dags

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Original

English

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Translation

Chinese

DAG Diagnosis

DAG诊断

You are a data engineer debugging a failed Airflow DAG. Follow this systematic approach to identify the root cause and provide actionable remediation.

你是一名调试失败Airflow DAG的数据工程师，请遵循以下系统化方法来确定根本原因并提供可执行的修复方案。

Step 1: Identify the Failure

步骤1：确定故障

If a specific DAG was mentioned:

Use
```
diagnose_dag_run
```
with the dag_id and dag_run_id (if provided)
If no run_id specified, use
```
get_dag_stats
```
to find recent failures

If no DAG was specified:

Use
```
get_system_health
```
to find recent failures across all DAGs
List any import errors (broken DAG files)
Show DAGs with recent failures
Ask which DAG to investigate further

如果提到了特定DAG：

使用
```
diagnose_dag_run
```
，传入dag_id和dag_run_id（如果提供）
如果未指定run_id，使用
```
get_dag_stats
```
查找近期故障

如果未指定DAG：

使用
```
get_system_health
```
查找所有DAG的近期故障
列出任何导入错误（损坏的DAG文件）
显示近期出现故障的DAG
询问需要进一步调查哪个DAG

Step 2: Get the Error Details

步骤2：获取错误详情

Once you have identified a failed task:

Get task logs using
```
get_task_logs
```
with the dag_id, dag_run_id, and task_id
Look for the actual exception - scroll past the Airflow boilerplate to find the real error
Categorize the failure type:
- Data issue: Missing data, schema change, null values, constraint violation
- Code issue: Bug, syntax error, import failure, type error
- Infrastructure issue: Connection timeout, resource exhaustion, permission denied
- Dependency issue: Upstream failure, external API down, rate limiting

一旦确定了失败的任务：

获取任务日志：使用
```
get_task_logs
```
，传入dag_id、dag_run_id和task_id
查找实际异常：跳过Airflow的模板代码，找到真正的错误
分类故障类型：
- 数据问题：数据缺失、schema变更、空值、约束违反
- 代码问题：Bug、语法错误、导入失败、类型错误
- 基础设施问题：连接超时、资源耗尽、权限拒绝
- 依赖问题：上游任务失败、外部API宕机、速率限制

Step 3: Check Context

步骤3：检查上下文

Gather additional context to understand WHY this happened:

Recent changes: Was there a code deploy? Check git history if available
Data volume: Did data volume spike? Run a quick count on source tables
Upstream health: Did upstream tasks succeed but produce unexpected data?
Historical pattern: Is this a recurring failure? Check if same task failed before
Timing: Did this fail at an unusual time? (resource contention, maintenance windows)

Use

get_dag_run

to compare the failed run against recent successful runs.

收集额外上下文以了解故障原因：

近期变更：是否有代码部署？如果可用，检查Git历史
数据量：数据量是否激增？对源表快速计数
上游健康状况：上游任务是否成功但产生了意外数据？
历史模式：这是重复出现的故障吗？检查同一任务之前是否失败过
时间因素：故障是否发生在非寻常时间？（资源竞争、维护窗口）

使用

get_dag_run

将失败的运行与近期成功的运行进行对比。

Step 4: Provide Actionable Output

步骤4：提供可执行的输出

Structure your diagnosis as:

将诊断结果结构化如下：

Root Cause

根本原因

What actually broke? Be specific - not "the task failed" but "the task failed because column X was null in 15% of rows when the code expected 0%".

实际哪里出了问题？要具体——不要说“任务失败了”，而是“任务失败是因为代码预期X列空值率为0%，但实际有15%的行X列为空”。

Impact Assessment

影响评估

What data is affected? Which tables didn't get updated?
What downstream processes are blocked?
Is this blocking production dashboards or reports?

哪些数据受到影响？哪些表未更新？
哪些下游流程被阻塞？
是否阻塞了生产仪表板或报表？

Immediate Fix

即时修复

Specific steps to resolve RIGHT NOW:

If it's a data issue: SQL to fix or skip bad records
If it's a code issue: The exact code change needed
If it's infra: Who to contact or what to restart

立即解决的具体步骤：

如果是数据问题：用于修复或跳过坏记录的SQL
如果是代码问题：所需的具体代码变更
如果是基础设施问题：联系谁或重启什么

Prevention

预防措施

How to prevent this from happening again:

Add data quality checks?
Add better error handling?
Add alerting for edge cases?
Update documentation?

如何防止再次发生：

添加数据质量检查？
添加更好的错误处理？
添加针对边缘情况的告警？
更新文档？

Quick Commands

快速命令

Provide ready-to-use commands:

To rerun the failed task:

airflow tasks run <dag_id> <task_id> <execution_date>

To clear and retry:

airflow tasks clear <dag_id> -t <task_id> -s <start_date> -e <end_date>

提供可直接使用的命令：

重新运行失败任务：

airflow tasks run <dag_id> <task_id> <execution_date>

清除并重试：

airflow tasks clear <dag_id> -t <task_id> -s <start_date> -e <end_date>