Debugging Dags

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.

On Astro

If you're running on Astro, these additional tools can help with diagnosis:

• Deployment activity log: Check the Astro UI for recent deploys — a failed deploy or recent code change is often the cause of sudden failures
• Astro alerts: Configure alerts in the Astro UI for proactive failure monitoring (DAG failure, task duration, SLA miss)
• Observability: Use the Astro observability dashboard to track DAG health trends and spot recurring issues

On OSS Airflow

• Airflow UI: Use the DAGs page, Graph view, and task logs to inspect recent runs and failures

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 the entire DAG run: af runs clear <dag_id> <run_id>
• To clear and rerun specific failed tasks: af tasks clear <dag_id> <run_id> <task_ids> -D
• To delete a stuck or unwanted run: af runs delete <dag_id> <run_id>