DAG Diagnosis

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

Step 1: Identify the Failure

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

Step 2: Get the Error Details

Once you have identified a failed task:

1. Get task logs using get_task_logs with the dag_id, dag_run_id, and task_id
2. Look for the actual exception – scroll past the Airflow boilerplate to find the real error
3. 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

Step 3: Check Context

Gather additional context to understand WHY this happened:

1. Recent changes: Was there a code deploy? Check git history if available
2. Data volume: Did data volume spike? Run a quick count on source tables
3. Upstream health: Did upstream tasks succeed but produce unexpected data?
4. Historical pattern: Is this a recurring failure? Check if same task failed before
5. 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.

Step 4: Provide Actionable Output

Structure your diagnosis as:

Root Cause

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%”.

Impact Assessment

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

Immediate Fix

Specific steps to resolve RIGHT NOW:

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

Prevention

How to prevent this from happening again:

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

Quick Commands

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>