debugging-dags

name

debugging-dags

description

Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline", "full root cause analysis", "why is this failing and how to prevent it". For simple debugging ("why did dag fail", "show logs"), the airflow entrypoint skill handles it directly. This skill provides structured investigation and prevention recommendations.

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.

Running the CLI

Run all af commands using uvx (no installation required):

uvx --from astro-airflow-mcp af <command>

Throughout this document, af is shorthand for uvx --from astro-airflow-mcp af.

---

Step 1: Identify the Failure

If a specific DAG was mentioned:

• Run af runs diagnose <dag_id> <dag_run_id> (if run_id is provided)
• If no run_id specified, run af dags stats to find recent failures

If no DAG was specified:

• Run af health to find recent failures across all DAGs
• Check for import errors with af dags errors
• 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 af tasks logs <dag_id> <dag_run_id> <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 af runs get <dag_id> <dag_run_id> 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 clear and rerun failed tasks: af tasks clear <dag_id> <run_id> <task_ids> -D